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--- surveys:mc [2010/02/15 22:10]
maysam.yabandeh
+++ surveys:mc [2010/03/01 10:11] (current)
maysam.yabandeh
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 ====== Model Checking Tools for Software Systems ======
+**//[[http://people.epfl.ch/maysam.yabandeh|Maysam Yabandeh]]//** [[http://infoscience.epfl.ch/record/146549/files/|pdf]]
+//Please add your comments into Section [[mc#Discussion]]//
 Systematic State Exploration or Model Checking techniques have been used for years to check the model of software against user-specified properties. Nevertheless, they never achieved a wide-spread usage because of the difficulties and problems in translating from the programming languages, which are used to develop the software, to the modeling language on which the model checker can work. Recently, there have been several efforts in direct state exploration of software system implementations. In this survey, we illustrate the challenges in this domain and explain the different solutions adopted by the state-of-the-art developed tools for state exploration of software systems.  The focus of this paper in on the developed model checking tools for software systems, and it does not include solutions for unit testing and selecting the optimal test scenarios.
@@ Line 7: / Line 12: @@
 Systematic State Exploration, which is also known as **model checking**, has been used for years to systematically explore reachable states in a model and to verify them against user-specified properties. The model can be made based on a software system, a hardware system, or any general phenomena. The languages that are used to develop software systems are different from the modeling language, and the developer has to go through the tedious, time-consuming task of translating the original program into the modeling language, i.e., **abstraction**. These difficulties discourage the developers to use the model checker tools for large software systems.
-To alleviate these problems, two general approaches have been used: i) automatically generating a model of the software, and ii) directly exploring the state space of the original software itself rather than a model of that. The former approach still suffers from the drawbacks of translation into a model; due to mismatches between the original software and the abstracted model, the found bugs are not sound. In other words, the reported bugs can not necessarily manifest in the original application as well.
+To alleviate these problems, two general approaches have been used: i) automatically generating a model of the software [[http://dblp.uni-trier.de/rec/bibtex/journals/sttt/BeyerHJM07|BLAST]] [[http://dblp.uni-trier.de/rec/bibtex/conf/popl/GodefroidNRT10|SMASH]], and ii) directly exploring the state space of the original software itself rather than a model of that. The former approach still suffers from the drawbacks of translation into a model; due to mismatches between the original software and the abstracted model, the found bugs are not sound. In other words, the reported bugs can not necessarily manifest in the original application as well.
+A recent example of approaches that automatically construct the abstract state space for exploration is [[http://dblp.uni-trier.de/rec/bibtex/journals/sttt/BeyerHJM07|BLAST]]. The estimated abstract model is an over-approximation of the actual state space with respect the checking predicates. In other words, the abstracted model is guaranteed to explore all the paths that leads to the violation of user-specified predicates, but the explored paths are not all feasible. BLAST uses the counterexamples to refine the abstract model. This process continues till either the the tool returns, proving no predicate violation, or return a path that violates the specified predicate. False negatives and positives are still positive due to mismatch between the programming language, C, and the abstracted model.
 The second approach, which is the focus of this paper, systematically explores the state space of the software system by controlling the scheduler and the inputs to the software. In this way, the developers can check the state space of their software without going through the error-prone, expensive task of abstraction. Although appealing, there are certain challenges in this approach that affect both applicability and efficiency of such tools. In this paper, we demonstrate these challenges and the different solutions adopted by the related tools.
@@ Line 13: / Line 20: @@
 In this paper, we refer to the employed algorithm for state exploration, as exploration algorithm or search algorithm. Given a explored state, the search algorithm invokes a property checker module on that. The property checker could check for deadlocks or user-specified properties which are also known as invariants. The assert statements inside the source code can either be checked by the property checker or their violation can be trapped and be reported to the developer.
-Similar to the tools for checking the models, the main challenge is still the state space explosion issue: the phenomena of exponentially increase in number of states by exploring deeper levels. However, here more number of non-determinisms as well as the large size of the real applications worsen this problem in a way that exhaustive exploration techniques become ineffective. Section [[mc#State Explosion Problem]] demonstrates this problem and its direct correlation with non-determinisms in the software systems. Section [[mc#Environment]] discusses that how we can alleviate the state explosion problem by more precise emulation of the environment. Section [[mc#Exploration Algorithm]], explains the changes in the exploration algorithms that can make efficient exploration of such large state spaces feasible. Finally, in Section [[mc#Exploration Algorithm]] we compare the state-of-the-art tools and illustrate how each of them addresses the described challenges.
+Similar to the tools for checking the models, the main challenge is still the state space explosion issue: the phenomena of exponentially increase in number of states by exploring deeper levels. However, here more number of non-determinisms as well as the large size of the real applications worsen this problem in a way that exhaustive exploration techniques become ineffective. Section [[mc#State Explosion Problem|link]] demonstrates this problem and its direct correlation with non-determinisms in the software systems. Section [[mc#Environment|link]] discusses that how we can alleviate the state explosion problem by more precise emulation of the environment. Section [[mc#Exploration Algorithm|link]], explains the changes in the exploration algorithms that can make efficient exploration of such large state spaces feasible. Finally, in Section [[mc#Exploration Algorithm|link]] we compare the state-of-the-art tools and illustrate how each of them addresses the described challenges.
 ===== State Explosion Problem =====
@@ Line 40: / Line 47: @@
 === Late Binding, Before Usage ===
-In late binding before usage, a set of branches covering the possible values is added right before the assigned variable is actually used~cite{gvero-delayed}. For example, in Figure {{:surveys:latebinding-statespace.gif?100|}}, which depicts the visited states following by the late-binding policy, the first branch is added right before Variable <latex>x</latex> is used at Line~3. The resulting state space by following the late binding policy is equivalent to the state space explored by early binding policy. However, it could potentially be more compact by merging equivalent states. The disadvantage is the slight added complexity for late binding.
+In late binding before usage, a set of branches covering the possible values is added right before the assigned variable is actually used [[http://infoscience.epfl.ch/record/128816|link]]. For example, in Figure {{:surveys:latebinding-statespace.gif?100|}}, which depicts the visited states following by the late-binding policy, the first branch is added right before Variable <latex>x</latex> is used at Line~3. The resulting state space by following the late binding policy is equivalent to the state space explored by early binding policy. However, it could potentially be more compact by merging equivalent states. The disadvantage is the slight added complexity for late binding.
 === Late Binding, During Property Evaluation ===
@@ Line 54: / Line 61: @@
 Not all the branches in the state graph that is obtained by symbolic execution are valid; some branches can be impossible to be traversed in a real run. The property checking module can help to prune some impossible branches. It can be used to evaluate the condition in the instruction; if the condition is evaluated as false, then the corresponding branch will be pruned, and vice versa. In Figure {{:surveys:se-statespace.gif?100|}}, the grayed branches are pruned from exploration. Consequently, the size of the space graph can be potentially much smaller than the case with early binding.
-The disadvantage of symbolic execution is that the complexity is pushed to the property checking module. However, there are well-known off-the-shelf SAT solvers, which take the history of operations performed on the symbolic variable as well as the list of assumed conditions and return weather the condition is satisfiable or not. The problem is that computation time of SAT solvers is not bounded, and it can take forever for a SAT solver to response. Besides, the size of the assumed condition set and the sequence of performed operations could become too large to be efficiently handled by state-of-the-art SAT solvers. For these reasons, Symbolic Execution can also quickly stick in the state explosion problem.
+The disadvantage of symbolic execution is that the complexity is pushed to the property checking module. However, there are well-known off-the-shelf SAT solvers, which take the history of operations performed on the symbolic variable as well as the list of assumed conditions and return whether the condition is satisfiable or not. The problem is that computation time of SAT solvers is not bounded, and it can take forever for a SAT solver to respond. Besides, the size of the assumed condition set and the sequence of performed operations could become too large to be efficiently handled by state-of-the-art SAT solvers. For these reasons, Symbolic Execution can also quickly get stuck with the state explosion problem.
-==== Modeling Environment ====
-The only thing that the state space exploration algorithm can be certain about is the sequence of program instructions. The sequence of states that will be explored by a program run depends on the particular environment that the program is deployed in.
-The **environment** is everything except the sequence of instructions in the program, which includes the hardware, the operating system, the communication environment, the input devices, the time, and also the other programs that will interact with the software.
-Every uncertainty about the environment introduces a non-determinism point into the search algorithm and worsens the state explosion problem. On the other hand, every assumption about the environment eliminates the corresponding non-determinism point and alleviates the state explosion problem.
-Another advantage of modeling the environment is eliminating the states which are impossible or improbable for the deployed software system to get into. For example, if we know that the input values to the system are always non-zero, we can ignore the branches that check for zero values. Beside the reduction in number of explored states, the search algorithm does not report the invariant violations that are impossible or improbable to occur in practice. Therefore the accuracy of the search algorithm increases and the number of false positive reports reduces.
-The disadvantage of modeling the environment is that the state exploration software becomes i) more complicated and ii) more environment dependent. For each model, we have to add some new logics that implement the model to the state exploration system, which makes the state exploration system complicated. On the other hand, every model makes some assumptions about the environment in which the software will be deployed. These assumptions could change from system to system or from time to time. However, the expenses for updating the model might not be trivial. For example, using a model of TCP rather than its actual implementation is one major source of complexity in the state exploration tools. Moreover, the model has to be updated when new versions of TCP are deployed.
-In the next section, we categorize the parts of the environment surrounding the software systems and discuss the employed techniques for modeling them or reducing uncertainty regarding them.
-===== Environment =====
-We define the environment as all the elements that will directly or indirectly affect the behavior of the deployed system. For each part of environment that we are uncertain about its behavior, we must explore all the possible actions and reactions in model checking. Uncertainty regarding each part of the environment can lead to more non-determinism points in the exploration process and consequently worsen the state explosion problem. By assuming a model for each part of the environment, we can further reduce the uncertainty and hence alleviate the exponential growth of the state space.
-In this section, we categorize the different parts of the environment and present different approaches that have been taken to reduce the uncertainty regarding each category. For a software system, we can split the environment into three general categories: i) the upper layer applications, which uses the provided service by the software, ii) the lower layer services, which supply the software with some services, and iii) the peers, which are the identical replicas of the software with whom the software interacts. In the following, we explain each category in more detail.
-====== Model Checking Tools ======
-Systematic State Exploration or Model Checking techniques have been used for years to check the model of software against user-specified properties. Nevertheless, they never achieved a wide-spread usage because of the difficulties and problems in translating from the programming languages, which are used to develop the software, to the modeling language on which the model checker can work. Recently, there have been several efforts in direct state exploration of software system implementations. In this survey, we illustrate the challenges in this domain and explain the different solutions adopted by the state-of-the-art developed tools for state exploration of software systems.  The focus of this paper in on the developed model checking tools for software systems, and it does not include solutions for unit testing and selecting the optimal test scenarios.
-===== Introduction =====
-Systematic State Exploration, which is also known as **model checking**, has been used for years to systematically explore reachable states in a model and to verify them against user-specified properties. The model can be made based on a software system, a hardware system, or any general phenomena. The languages that are used to develop software systems are different from the modeling language, and the developer has to go through the tedious, time-consuming task of translating the original program into the modeling language, i.e., **abstraction**. These difficulties discourage the developers to use the model checker tools for large software systems.
-To alleviate these problems, two general approaches have been used: i) automatically generating a model of the software, and ii) directly exploring the state space of the original software itself rather than a model of that. The former approach still suffers from the drawbacks of translation into a model; due to mismatches between the original software and the abstracted model, the found bugs are not sound. In other words, the reported bugs can not necessarily manifest in the original application as well.
-The second approach, which is the focus of this paper, systematically explores the state space of the software system by controlling the scheduler and the inputs to the software. In this way, the developers can check the state space of their software without going through the error-prone, expensive task of abstraction. Although appealing, there are certain challenges in this approach that affect both applicability and efficiency of such tools. In this paper, we demonstrate these challenges and the different solutions adopted by the related tools.
-In this paper, we refer to the employed algorithm for state exploration, as exploration algorithm or search algorithm. Given a explored state, the search algorithm invokes a property checker module on that. The property checker could check for deadlocks or user-specified properties which are also known as invariants. The assert statements inside the source code can either be checked by the property checker or their violation can be trapped and be reported to the developer.
-Similar to the tools for checking the models, the main challenge is still the state space explosion issue: the phenomena of exponentially increase in number of states by exploring deeper levels. However, here more number of non-determinisms as well as the large size of the real applications worsen this problem in a way that exhaustive exploration techniques become ineffective. Section [[mc#State Explosion Problem]] demonstrates this problem and its direct correlation with non-determinisms in the software systems. Section [[mc#Environment]] discusses that how we can alleviate the state explosion problem by more precise emulation of the environment. Section [[mc#Exploration Algorithm]], explains the changes in the exploration algorithms that can make efficient exploration of such large state spaces feasible. Finally, in Section [[mc#Exploration Algorithm]] we compare the state-of-the-art tools and illustrate how each of them addresses the described challenges.
-===== State Explosion Problem =====
-The processing unit (CPU) that runs a software program computes only the single next state of the system by executing the next instruction in the program.
-Figure  depicts the state space explored by running the sample program presented in Figure<latex>bool $x$ = read(); bool $y$ = read(); if ($x$ and $y$) $z$ = 5; else $z$ = 8; if ($x$) $z$ = 2 * $z$; else $z$ = 0; </latex>.
-In contrary to that, more than one state can result from an instruction execution in systematic state space exploration.
-For example, when an if-then-else instruction checks for a variable that its value is received from an input device such as keyboard, the next state of the system depends on the concrete value of the variable. Although this value will be determined at run-time, during state space exploration it is a source of **non-determinism** for the search algorithm.
-Corresponding to each non-determinism point, a branch will be introduced to the state space to cover all the possible options. Taking each branch leads the system to a potentially different state. The number of reachable states exponentially increases with the number of branches, which is known as **state explosion** phenomenon.
-==== State Space ====
-As mentioned before, the space of potential states that a software can visit is way larger than the set of visited states by a particular run. For example, the state space of the snippet of code shown in Figure<latex>bool $x$ = read(); bool $y$ = read(); if ($x$ and $y$) $z$ = 5; else $z$ = 8; if ($x$) $z$ = 2 * $z$; else $z$ = 0; </latex>
- is illustrated in Figure {{:surveys:whole-statespace.gif?100}}. As you can observe, in contrary to the visited states by running the program, the state space is not sequential; there exist branches corresponding to each non-determinism point. Each set of joint branches bind some values to a particular non-determinism point. For example, corresponding to Variable <latex>x</latex> which be assigned at run-time, there is a branch at Figure {{:surveys:whole-statespace.gif?100}} to cover both values **true** and **false**.
-The policy that specified the position that a branch appears in the state graph is determined by the exploration algorithm. The policy defines when the possible values should be bound to the non-determinism points.
-=== Early Binding ===
-In early binding, a branch covering the possible values is added when the variable is assigned. For example, in the snippet of code shown in Figure
-<latex>bool $x$ = read(); bool $y$ = read(); if ($x$ and $y$) $z$ = 5; else $z$ = 8; if ($x$) $z$ = 2 * $z$; else $z$ = 0; </latex>
-, Boolean <latex>x</latex> is assigned by reading from keyboard. In Figure {{:surveys:whole-statespace.gif?100}}, which is the visited states following by the early-binding policy, the first branch is added immediately after assigning the value to Boolean <latex>x</latex> in the program.
-=== Late Binding, Before Usage ===
-In late binding before usage, a set of branches covering the possible values is added right before the assigned variable is actually used~cite{gvero-delayed}. For example, in Figure {{:surveys:latebinding-statespace.gif?100|}}, which depicts the visited states following by the late-binding policy, the first branch is added right before Variable <latex>x</latex> is used at Line~3. The resulting state space by following the late binding policy is equivalent to the state space explored by early binding policy. However, it could potentially be more compact by merging equivalent states. The disadvantage is the slight added complexity for late binding.
-=== Late Binding, During Property Evaluation ===
-The binding can be postponed till the time the model checker evaluates the user-specified properties against the system state. Since the concrete value is not bound, a **symbolic** value is kept for the variable. The model checker then has to keep track of the operations performed on the symbolic value and pass them to the property checking module. This technique is called **Symbolic Execution**.
-The major challenge in symbolic execution is to executing **conditional branch** instructions. Conditional branch in programming languages is the essential part for implementation of if-then-else and loop statements. (Note that conditional branches are different from the branches in state space graph.) In the normal run of the program, the CPU jumps to the position specified by the instruction, if the corresponding condition is evaluated to true. In symbolic execution, the model checker does not have the concrete values to evaluate the condition. Therefore, a branch is added to the state space to cover both true and false values. In Figure {{:surveys:se-statespace.gif?100|}}, the first branch is added right before evaluating <latex>x</latex> \&\& <latex>y</latex> in the first if-then-else statement of the code snippet shown in Figure
-<latex>bool $x$ = read(); bool $y$ = read(); if ($x$ and $y$) $z$ = 5; else $z$ = 8; if ($x$) $z$ = 2 * $z$; else $z$ = 0; </latex>
-. By taking each branch, an assumption is made about the evaluation of the condition. The set of assumptions made along the path will be passed to the property checking module.
-Note that the state space graph obtained by symbolic execution is not equivalent to the state space graph obtained by early binding. Here, the branches in the graph correspond to branch points in the program structure. Corresponding to each if-then-else statement, a branch is added to the current position at state graph.
-Not all the branches in the state graph that is obtained by symbolic execution are valid; some branches can be impossible to be traversed in a real run. The property checking module can help to prune some impossible branches. It can be used to evaluate the condition in the instruction; if the condition is evaluated as false, then the corresponding branch will be pruned, and vice versa. In Figure {{:surveys:se-statespace.gif?100|}}, the grayed branches are pruned from exploration. Consequently, the size of the space graph can be potentially much smaller than the case with early binding.
-The disadvantage of symbolic execution is that the complexity is pushed to the property checking module. However, there are well-known off-the-shelf SAT solvers, which take the history of operations performed on the symbolic variable as well as the list of assumed conditions and return weather the condition is satisfiable or not. The problem is that computation time of SAT solvers is not bounded, and it can take forever for a SAT solver to response. Besides, the size of the assumed condition set and the sequence of performed operations could become too large to be efficiently handled by state-of-the-art SAT solvers. For these reasons, Symbolic Execution can also quickly stick in the state explosion problem.
 ==== Modeling Environment ====
@@ Line 154: / Line 88: @@
 However, considering all the possible values for input parameters is not always feasible. For example, for a 32-bit integer variable the number of possible values is <latex>2^{32}</latex>. Having no model for the upper layer, the exploration algorithm has to check all these values to achieve a complete search. (Symbolic execution techniques can check for much less number of values by considering a symbolic value for the variables instead of concrete values.) The test driver plays the role of the model for the upper layer application by focusing on a limited set of application requests. For instance, a test driver for a database service sends a particular set of queries to the database software.
-The test drivers are obviously not complete. There is a trade-off between the completeness and feasibility in systematic exploration. In the case of large software systems, it is inevitable to sacrifice the completeness for feasibility of the search. In Section [[mc#Exploration Algorithm]] we will see that the exhaustive exploration algorithms have to be bounded to some depths anyway. More accurately selected test scenarios would lead to systematic exploration of more relevant and important states of the software system.
+The test drivers are obviously not complete. There is a trade-off between the completeness and feasibility in systematic exploration. In the case of large software systems, it is inevitable to sacrifice the completeness for feasibility of the search. In Section [[mc#Exploration Algorithm|link]] we will see that the exhaustive exploration algorithms have to be bounded to some depths anyway. More accurately selected test scenarios would lead to systematic exploration of more relevant and important states of the software system.
 ==== Lower layer services ====
@@ Line 209: / Line 143: @@
 Models can hide the complexities of the operating system services and increase state exploration performance. For example, a PIPE in Linux operating system can be modeled by a simple queue structure. The problem with models is that they are valid as long as conform to the implementation of the original service. In the case of complex services such as TCP, the model is not trivial and can be very complicated. This increases the risk of a mistake in modeling the service as well as expenses of updating the model according to the new changes in the service implementation. The other advantage of using models is that due to simplification in the model, the state of the model is much simpler and more controllable. Thus, it will be feasible to reproduce a series of events on them.
-The expenses of modeling the operating system and its maintenance increase with the increase in the number of operating system services. For example, Windows offers more than 100 system calls~cite{yang09}; modeling all these system call in an operating system which has more than one million lines of code is very expensive and unreliable.
+The expenses of modeling the operating system and its maintenance increase with the increase in the number of operating system services. For example, Windows offers more than 100 system calls [[http://www.usenix.org/events/nsdi09/tech/full_papers/yang/yang.pdf|link]]; modeling all these system call in an operating system which has more than one million lines of code is very expensive and unreliable.
 ==== Peers ====
@@ Line 219: / Line 153: @@
 The other approach is to start the exploration with <latex>N</latex> peers where <latex>N</latex> is a fixed number. If we take <latex>N</latex> small, the inconsistencies that would only manifest for larger <latex>N</latex> stay undetected. On the other hand, the number of states increases exponentially with the increase in peer count, and the state exploration, hence, is impractical for large values of <latex>N</latex>.
-Some related works~cite{yabandeh09} take the middle ground: they start the state exploration for a large number of peers, <latex>N</latex>. However the exploration algorithm executes only the events that are related to a small set of nodes (with size of <latex>M</latex>), and ignores the events which are related to the other nodes. For <latex>M \ll N</latex>, the state exploration can be efficient although it only partly stresses the system.
+Some related works [[http://www.usenix.org/event/nsdi09/tech/full_papers/yabandeh/yabandeh.pdf|link]] take the middle ground: they start the state exploration for a large number of peers, <latex>N</latex>. However the exploration algorithm executes only the events that are related to a small set of nodes (with size of <latex>M</latex>), and ignores the events which are related to the other nodes. For <latex>M \ll N</latex>, the state exploration can be efficient although it only partly stresses the system.
 ===== Exploration Algorithm =====
@@ Line 233: / Line 167: @@
 In BDFS algorithm, instead of the full system state we can only keep the index of the picked items from the root till the bottom of the state space. We call this I-BDFS. When the algorithm reaches the maximum depth, all the states along the path from root to the current state are checked. To backtrack it needs to obtain the system state for the last step. It obtains the last state by starting over from the root and executing the same sequence of events (picking the same index from the ready event list). Since the maximum achievable depth is shallow anyway, the expenses of re-executing the events are often less than expensive operations of storing and loading states from memory.
-The challenge in I-BDFS is reproducibility of the event sequence; after executing the same sequence of events we expect the system to reach the same state. As we discussed in Section [[mc#Environment]], there are lots of non-determinism in the environment which are not necessarily under control of the exploration algorithm. For example, the implementation of a system call inside the operating system might use some random values which are different at each run. Using models can address this problem since the model are intentionally developed to be controllable.
+The challenge in I-BDFS is reproducibility of the event sequence; after executing the same sequence of events we expect the system to reach the same state. As we discussed in Section [[mc#Environment|link]], there are lots of non-determinism in the environment which are not necessarily under control of the exploration algorithm. For example, the implementation of a system call inside the operating system might use some random values which are different at each run. Using models can address this problem since the model are intentionally developed to be controllable.
 ==== Stateless or Stateful ====
@@ Line 247: / Line 181: @@
 The POR techniques prune the state space of a concurrent system to avoid unnecessary interleaving of events. For example, if executing <latex><s_0 \xrightarrow{e_1} s_1 \xrightarrow{e_2} s_2></latex> and <latex><s_0 \xrightarrow{e_2} s_1' \xrightarrow{e_1} s_2></latex> result in the same state, exploring only one of them is enough for checking the invariants of the software system. In this case, <latex>e_1</latex> and <latex>e_2</latex> are called independent. Independence is not enough to prune <latex>e_2</latex> from the state space graph. It is because of the fact that there might be other events enabled at state <latex>s_1'</latex> that following them gets the system into states which are not reachable from <latex>s_1</latex>. To be able to prune <latex>e_2</latex>, we must first prove that <latex>e_1</latex> and <latex>e_2</latex> are in a **persistent** set at <latex>s_0</latex>.
-Obtaining independent events and persistent sets requires static analysis on the source code. Static analysis tools might not be available in all programming languages; specifically, if the environment (such as operating system) is included in the analysis. For example, independence of two operations which use different system calls is not easy to prove. Furthermore, it is shown that static analysis is not efficient in dealing with dynamic data such as pointers~cite{flanagan05dynamicPOR}. This is because the value referenced by the pointer is not available at the time of analysis.
+Obtaining independent events and persistent sets requires static analysis on the source code. Static analysis tools might not be available in all programming languages; specifically, if the environment (such as operating system) is included in the analysis. For example, independence of two operations which use different system calls is not easy to prove. Furthermore, it is shown that static analysis is not efficient in dealing with dynamic data such as pointers [[http://dblp.uni-trier.de/rec/bibtex/conf/popl/GodefroidNRT10|link]]. This is because the value referenced by the pointer is not available at the time of analysis.
-Dynamic Partial Order Reduction (DPOR)~cite{flanagan05dynamicPOR} is designed to solve the limitations of static analysis. The DPOR algorithm, computes the dependency during exploration, when the concrete values of the pointers are available. According to the observed dependencies, it adds appropriate branches to guaranty the completeness of the exploration. The limitation of DPOR is that it works only for multi-threaded programs and is not applicable to distributed systems. DPOR-DS inspired from the main insight of DPOR and design an algorithm for distributed systems~cite{dpords}.
+Dynamic Partial Order Reduction (DPOR) [[http://dblp.uni-trier.de/rec/bibtex/conf/popl/GodefroidNRT10|link]] is designed to solve the limitations of static analysis. The DPOR algorithm, computes the dependency during exploration, when the concrete values of the pointers are available. According to the observed dependencies, it adds appropriate branches to guaranty the completeness of the exploration. The limitation of DPOR is that it works only for multi-threaded programs and is not applicable to distributed systems. DPOR-DS inspired from the main insight of DPOR and design an algorithm for distributed systems [[http://infoscience.epfl.ch/record/139173|link]].
 There are other techniques based on static analysis named by **sleep sets** which are beyond the scope of this paper.
@@ Line 257: / Line 191: @@
 Where more than one process are being model checked, the model checker should consider different interleavings between the processes. This is because the process shared variables can change by other processes. Therefore, after executing each atomic instruction, the model checker should add a branch for the case that the thread is preempted and another thread carries on.
-By taking big steps, the model checker assumes a sequence of instructions as a big atomic instruction and do not preempt the process in the middle of their execution. Obviously, it alleviates the state explosion problem by reducing the number of branches. For example, Chess~cite{musuvathi2008finding} limit the number of preemptions per each thread in model checking of multi-threaded programs. In this case, big step is a trade-off between completeness of the exploration and its feasibility in a limited time.
+By taking big steps, the model checker assumes a sequence of instructions as a big atomic instruction and do not preempt the process in the middle of their execution. Obviously, it alleviates the state explosion problem by reducing the number of branches. For example, Chess [[http://dblp.uni-trier.de/rec/bibtex/conf/osdi/MusuvathiQBBNN08|link]] limit the number of preemptions per each thread in model checking of multi-threaded programs. In this case, big step is a trade-off between completeness of the exploration and its feasibility in a limited time.
-Taking big steps does not necessarily make the exploration incomplete. For example, the sequence of instructions that do not touch shared variables can be assumed atomic, since preemption in the middle of them is equivalent with the preemption after them~cite{flanagan05dynamicPOR}. MaceMC~cite{killian07life} also takes the whole instructions inside a handler as atomic. The exploration is complete since the Mace system is event-based and the handler code will not be interrupted with another handler execution.
+Taking big steps does not necessarily make the exploration incomplete. For example, the sequence of instructions that do not touch shared variables can be assumed atomic, since preemption in the middle of them is equivalent with the preemption after them [[http://dblp.uni-trier.de/rec/bibtex/conf/popl/GodefroidNRT10|link]]. MaceMC [[http://dblp.uni-trier.de/rec/bibtex/conf/nsdi/KillianAJV07|link]] also takes the whole instructions inside a handler as atomic. The exploration is complete since the Mace system is event-based and the handler code will not be interrupted with another handler execution.
 From a high-level point of view, models of the environment always take big steps. Each operation on the model can be equivalent to multiple steps in the real environment counterpart, which potentially can be interrupted.
@@ Line 273: / Line 207: @@
 === Random walk ===
-The simplest form of exploration heuristic is random walk: to pick one event from the ready event list and expand only that particular branch of the state graph. The random walk can go very deep in the state space. Nevertheless, it also misses exploring some states that are accessible from the initial state by a few steps. One intuitive way to address this problem is combining the exhaustive search and random walks. The different possible combinations of these two are discussed in~cite{sivaraj2003random}.
+The simplest form of exploration heuristic is random walk: to pick one event from the ready event list and expand only that particular branch of the state graph. The random walk can go very deep in the state space. Nevertheless, it also misses exploring some states that are accessible from the initial state by a few steps. One intuitive way to address this problem is combining the exhaustive search and random walks. The different possible combinations of these two are discussed in [[http://dblp.uni-trier.de/rec/bibtex/journals/entcs/SivarajG03|link]].
-In the pure random walk, the likelihoods of exploring a very rare state and a very common state are the same. Depending on the objectives of the testing, it might be more desirable to explore the states that will be mostly visited after deployment of the system. One approach is to assign weights to the events and randomly pick an event from the ready event list according to their weights. For example, the chance of a packet drop is very low in the network and the assigned weight to that could be low. A more complicated approach can analyze the log files to obtain the probability of different sequence of events. **Bayesian networks**~cite{judea1985bayesian} sounds like a right match for this purpose.
+In the pure random walk, the likelihoods of exploring a very rare state and a very common state are the same. Depending on the objectives of the testing, it might be more desirable to explore the states that will be mostly visited after deployment of the system. One approach is to assign weights to the events and randomly pick an event from the ready event list according to their weights. For example, the chance of a packet drop is very low in the network and the assigned weight to that could be low. A more complicated approach can analyze the log files to obtain the probability of different sequence of events. **Bayesian networks** [[http://ftp.cs.ucla.edu/tech-report/198_-reports/850017.pdf|link]] sounds like a right match for this purpose.
 === Initial state ===
-The root state in the explored state graph is normally the initial state of the software system and the environment. Due to state explosion problem, the depth of a complete exploration would be limited to few steps. This does not allow the system to be stressed against complicated configurations. Starting a complete exploration after a long random walk would alleviate this problem. In~cite{killian07life}, it is proposed to disable the faulty events such as packet drop and connection break during the random walk. This allows the system to get into a stable state before starting the complete search.
+The root state in the explored state graph is normally the initial state of the software system and the environment. Due to state explosion problem, the depth of a complete exploration would be limited to few steps. This does not allow the system to be stressed against complicated configurations. Starting a complete exploration after a long random walk would alleviate this problem. In [[http://dblp.uni-trier.de/rec/bibtex/conf/nsdi/KillianAJV07|link]], it is proposed to disable the faulty events such as packet drop and connection break during the random walk. This allows the system to get into a stable state before starting the complete search.
-Another approach is to obtain a system state from an actual live run and use this state as the initial state in the exploration~cite{yabandeh09}. The advantage of this approach is that the exploration starts from a state in which the system has gone through complicated interleaving of events. Moreover, the explored states would be more relevant as they are accessible from a state taken from the live run. After a few steps, the exploration can be restarted from another state also taken from the live run.
+Another approach is to obtain a system state from an actual live run and use this state as the initial state in the exploration [[http://www.usenix.org/event/nsdi09/tech/full_papers/yabandeh/yabandeh.pdf|link]]. The advantage of this approach is that the exploration starts from a state in which the system has gone through complicated interleaving of events. Moreover, the explored states would be more relevant as they are accessible from a state taken from the live run. After a few steps, the exploration can be restarted from another state also taken from the live run.
 === Event Interleaving ===
-The non-determinism in event interleaving is a major contributor to exponential growth of the state space. POR techniques, which are sound and complete, alleviate this problem slightly. Nevertheless, the state explosion still manifests after a few steps. Because of that, most of the developed tools for model checking of software systems were forced to eventually rely on random walks~cite{killian07life,yang09}.
+The non-determinism in event interleaving is a major contributor to exponential growth of the state space. POR techniques, which are sound and complete, alleviate this problem slightly. Nevertheless, the state explosion still manifests after a few steps. Because of that, most of the developed tools for model checking of software systems were forced to eventually rely on random walks [[http://dblp.uni-trier.de/rec/bibtex/conf/nsdi/KillianAJV07|link1]]  [[http://www.usenix.org/events/nsdi09/tech/full_papers/yang/yang.pdf|link2]].
-Realistically speaking, in large software systems the completeness property of POR techniques is not appealing as much. Therefore, the heuristics which are not complete but leads the search to more relevant states are more interesting. One example is **Consequence Prediction**, which is proposed in CrystalBall~cite{yabandeh09}. It filters a non-network handler, if the handler is already run on the same node local state, i.e., the process state.
+Realistically speaking, in large software systems the completeness property of POR techniques is not appealing as much. Therefore, the heuristics which are not complete but leads the search to more relevant states are more interesting. One example is **Consequence Prediction**, which is proposed in CrystalBall [[http://www.usenix.org/event/nsdi09/tech/full_papers/yabandeh/yabandeh.pdf|link]]. It filters a non-network handler, if the handler is already run on the same node local state, i.e., the process state.
 ===== Related Tools =====
-In this section, we explain the design of the developed tools for model checking of software systems implementations.
+In this section, we explain the design of the developed tools for model checking of software systems implementations. Here we only focus on a subset of the tools which have shown to work on large software systems.
 ==== Verisoft ====
-To avoid challenges of large state size in software systems, Verisoft~cite{godefroid1997model} takes the stateless approach. POR techniques are then applied to alleviate the drawbacks of the stateless approach. Since efficient persistent sets require information about the static program structure, POR techniques used in Verisoft are mostly successful in reducing the number of transitions (because of using sleep sets) rather than number of visited states.
+To avoid challenges of large state size in software systems, Verisoft [[http://dblp.uni-trier.de/rec/bibtex/conf/popl/Godefroid97|link]] takes the stateless approach. POR techniques are then applied to alleviate the drawbacks of the stateless approach. Since efficient persistent sets require information about the static program structure, POR techniques used in Verisoft are mostly successful in reducing the number of transitions (because of using sleep sets) rather than number of visited states.
 It uses test drivers as a model for the application layer. The test driver should use Verisoft specialized functions: VS\_toss and VS\_assert.
@@ Line 306: / Line 240: @@
 Verisoft takes big steps in model checking by dividing the instructions in two visible and invisible groups. A visible instruction executes an operation on a shared object. The set of invisible instructions between two visible ones are considered atomic with the last visible instruction.
-A free download is available at~cite{verisoft}.
+A free download is available at [[http://cm.bell-labs.com/who/god/verisoft|link]].
 ==== Java Pathfinder ====
-Java Pathfiner (JPF)~cite{havelund2000jpf}, is an explicit state model checker for Java bytecode.
+Java Pathfiner (JPF) [[http://dblp.uni-trier.de/rec/bibtex/conf/spin/VisserM05|link]], is an explicit state model checker for Java bytecode.
 It checks for deadlocks and user-specified assert statements.
 JPF follows the stateful approach for state exploration. The Java Virtual Machine (JVM) is instrumented to store/load the application and exploration algorithm states.
@@ Line 319: / Line 253: @@
 It applies on-the-fly POR, which is similar in spirit to DPOR, to alleviate the state explosion problem. To identify dependent operations at run-time, they monitor read/write operations on the shared objects. To make it less expensive, they suggest that the monitoring piggybacks on garbage collection.
-In the original version of JPF, the upper layer application must be modeled by a test driver. The recent version of JPF is instrumented with symbolic execution to address this limitation~cite{visser2004jpf}. The developed techniques tackle the aliasing and dynamically generated objects. They use late binding (called lazy initialization) to make the state space graph smaller. Note that the environment must still be modeled.
+In the original version of JPF, the upper layer application must be modeled by a test driver. The recent version of JPF is instrumented with symbolic execution to address this limitation [[http://dblp.uni-trier.de/rec/bibtex/conf/issta/VisserPK04|link]]. The developed techniques tackle the aliasing and dynamically generated objects. They use late binding (called lazy initialization) to make the state space graph smaller. Note that the environment must still be modeled.
-The source code is available at~cite{jpf}.
+The source code is available at [[http://javapathfinder.sourceforge.net|link]].
 ==== CMC ====
-In contrast to Verisoft~cite{godefroid1997model}, CMC~cite{musuvathi02} takes the stateful approach for model checking of C programs. The global variables and the heap content is stored and loaded for switching the state. The user also specifies some memory locations that she thinks are not necessary for the model checking purpose, to be eliminated from the saved states.
+In contrast to Verisoft [[http://dblp.uni-trier.de/rec/bibtex/conf/popl/Godefroid97|link]], CMC [[http://doi.acm.org/10.1145/844128.844136|link]] takes the stateful approach for model checking of C programs. The global variables and the heap content is stored and loaded for switching the state. The user also specifies some memory locations that she thinks are not necessary for the model checking purpose, to be eliminated from the saved states.
 CMC applies on event-driven applications, and the whole implementation of a handler is taken as a big atomic step. However, the user has to manually specify the handler boundaries. The user also has to define some functions for initialization. CMC checks for user-specified asserts as well as memory leaks.
@@ Line 337: / Line 271: @@
 ==== MaceMC ====
-CMC~cite{musuvathi02} requires user involvement in various phases such as specifying initialization functions, the handler boundaries, and the important parts of state. MaceMC~cite{killian07life} takes advantage of the fact that these steps are mostly done in structured programs written in Mace framework~cite{killian07mace}. In Mace language, the initialization function and the handler boundaries are part of the language. The framework also offers some utility functions such as serialization of state into a stream.
+CMC [[http://doi.acm.org/10.1145/844128.844136|link]] requires user involvement in various phases such as specifying initialization functions, the handler boundaries, and the important parts of state. MaceMC [[http://dblp.uni-trier.de/rec/bibtex/conf/nsdi/KillianAJV07|link]] takes advantage of the fact that these steps are mostly done in structured programs written in Mace framework[[http://doi.acm.org/10.1145/1250734.1250755|link]]. In Mace language, the initialization function and the handler boundaries are part of the language. The framework also offers some utility functions such as serialization of state into a stream.
 Similar to CMC, the big steps are specified by handler boundaries. The upper layer application is modeled by a test program. In Mace language, the used services by the program are explicitly specified in the source code. During model checking, the user can make an instance of such services in the test driver and pass them to the Mace program. The alternative solution is to make a corresponding model class and pass it instead. The native operating system services are not modeled and hence are beyond the control of MaceMc. It, however, offers some wrappers for services such as UDP and TCP, and the developer is encouraged to use them. These wrappers must be replaced with corresponding models offered by Mace, in the test drivers.
@@ Line 345: / Line 279: @@
 The offered randint() function for random values is instrumented to cover the whole range during model checking. Moreover, if the developer uses the specialized time function offered by Mace, it will be replaced by a monotonic counter during model checking.
-The source code is available at~cite{macemc}.
+The source code is available at [[http://www.macesystems.org|link]].
 ==== Modist ====
-Modist~cite{yang09} is a stateless model checker for unmodified distributed systems in Windows. The big steps for model checking are the code between two system calls. It instruments the application binary to replace the system calls with some API wrappers. The wrapper mostly contacts the exploration back-end with RPC and then invokes the original system call or returns failure, depending on the back-end response. In the case of networking APIs, the wrapper redirects the call from the original networking API to a network model. The model is implemented by an asynchronous IO channel as well as a proxy thread for intercepting the packets. Therefore, the other processes can be located on remote machines.
+Modist  [[http://www.usenix.org/events/nsdi09/tech/full_papers/yang/yang.pdf|link]] is a stateless model checker for unmodified distributed systems in Windows. The big steps for model checking are the code between two system calls. It instruments the application binary to replace the system calls with some API wrappers. The wrapper mostly contacts the exploration back-end with RPC and then invokes the original system call or returns failure, depending on the back-end response. In the case of networking APIs, the wrapper redirects the call from the original networking API to a network model. The model is implemented by an asynchronous IO channel as well as a proxy thread for intercepting the packets. Therefore, the other processes can be located on remote machines.
 The time issue is addressed by using symbolic execution starting from the invocation position of the time function. The back-end tries different values of time that cover all the branches in the code till a certain depth. The paper does not propose a solution to random functions and their appearance in the application or the operating system can cause difficulties in deterministically replaying the error path.
@@ Line 355: / Line 289: @@
 The source code is not publicly available.
 ===== Conclusions =====
@@ Line 365: / Line 300: @@
 Time and random values are still two big problems in model checking. Although application-specific solutions have been proposed, there is still no approach to force the developers to use them. Perhaps, the future programming languages can be **model checking-aware** in the sense that they force the developers to use only the mechanisms that are already instrumented to be used in model checkers.
+===== Discussion =====
+Q: How about BLAST?
-==== Java Pathfinder ====
+A: It is added now.
-Java Pathfiner (JPF)~cite{havelund2000jpf}, is an explicit state model checker for Java bytecode.
-It checks for deadlocks and user-specified assert statements.
-JPF follows the stateful approach for state exploration. The Java Virtual Machine (JVM) is instrumented to store/load the application and exploration algorithm states.
-JPF offers specialized methods for picking a random value, Verify.randomInt($n$). It also offers a random function for double values, Verify.randomDouble(). However, the returned values do not systematically cover the whole range, and a user-defined heuristic model is used to choose only one single returned value.
+----
-To model the environment, the user has to write some model classes which emulate the environment. For each method in the model classes, JPF automatically intercepts the corresponding calls from the application and return the control to the model class (instead of original class in JVM).
+Q: How about SMASH?
-It applies on-the-fly POR, which is similar in spirit to DPOR, to alleviate the state explosion problem. To identify dependent operations at run-time, they monitor read/write operations on the shared objects. To make it less expensive, they suggest that the monitoring piggybacks on garbage collection.
+A: It suffers from the same problems as BLAST does. Abstract based model checking is not in the scope of this paper and citing the paper is enough. I cited the paper in the introduction.
-In the original version of JPF, the upper layer application must be modeled by a test driver. The recent version of JPF is instrumented with symbolic execution to address this limitation~cite{visser2004jpf}. The developed techniques tackle the aliasing and dynamically generated objects. They use late binding (called lazy initialization) to make the state space graph smaller. Note that the environment must still be modeled.
-The source code is available at~cite{jpf}.
-==== CMC ====
-In contrast to Verisoft~cite{godefroid1997model}, CMC~cite{musuvathi02} takes the stateful approach for model checking of C programs. The global variables and the heap content is stored and loaded for switching the state. The user also specifies some memory locations that she thinks are not necessary for the model checking purpose, to be eliminated from the saved states.
-CMC applies on event-driven applications, and the whole implementation of a handler is taken as a big atomic step. However, the user has to manually specify the handler boundaries. The user also has to define some functions for initialization. CMC checks for user-specified asserts as well as memory leaks.
-The upper layer application is modeled by test programs. The operating system calls and specially the network calls are also replaced by some models. To model time, CMC offers a specialized function to obtain the current time, gettimeofday(), which will be replaced with an autonomic counter during model checking.
-The random values also can be obtained by invoking CMCChoose() function. During model checking, the returned values will cover the whole range of options.
-The paper, however, does not specify that how it motivates the users to use only this particular offered functions.
-The exploration algorithm is BFS. Since the elements in queue are referenced in order, the queue of states in BFS can be kept mostly in hard disk rather than memory. This alleviates the problem of keeping track of large states. Nevertheless, loading and storing of large states still is a time-consuming task.
-To my knowledge, the source code of CMC is not available.
-==== MaceMC ====
-CMC~cite{musuvathi02} requires user involvement in various phases such as specifying initialization functions, the handler boundaries, and the important parts of state. MaceMC~cite{killian07life} takes advantage of the fact that these steps are mostly done in structured programs written in Mace framework~cite{killian07mace}. In Mace language, the initialization function and the handler boundaries are part of the language. The framework also offers some utility functions such as serialization of state into a stream.
-Similar to CMC, the big steps are specified by handler boundaries. The upper layer application is modeled by a test program. In Mace language, the used services by the program are explicitly specified in the source code. During model checking, the user can make an instance of such services in the test driver and pass them to the Mace program. The alternative solution is to make a corresponding model class and pass it instead. The native operating system services are not modeled and hence are beyond the control of MaceMc. It, however, offers some wrappers for services such as UDP and TCP, and the developer is encouraged to use them. These wrappers must be replaced with corresponding models offered by Mace, in the test drivers.
-The exploration algorithm is stateful and consists from a combination of I-BDFS followed by random walks. The state is loaded only for creating the initial state and the intermediate states are obtained by rerunning the event handlers.
-The offered randint() function for random values is instrumented to cover the whole range during model checking. Moreover, if the developer uses the specialized time function offered by Mace, it will be replaced by a monotonic counter during model checking.
-The source code is available at~cite{macemc}.
-==== Modist ====
-Modist~cite{yang09} is a stateless model checker for unmodified distributed systems in Windows. The big steps for model checking are the code between two system calls. It instruments the application binary to replace the system calls with some API wrappers. The wrapper mostly contacts the exploration back-end with RPC and then invokes the original system call or returns failure, depending on the back-end response. In the case of networking APIs, the wrapper redirects the call from the original networking API to a network model. The model is implemented by an asynchronous IO channel as well as a proxy thread for intercepting the packets. Therefore, the other processes can be located on remote machines.
-The time issue is addressed by using symbolic execution starting from the invocation position of the time function. The back-end tries different values of time that cover all the branches in the code till a certain depth. The paper does not propose a solution to random functions and their appearance in the application or the operating system can cause difficulties in deterministically replaying the error path.
-The exploration algorithms are a heuristic inspired from DPOR, as well as random walks. To alleviate the state explosion problem, the number of injected faults into the system calls is limited.
-The source code is not publicly available.
-===== Conclusions =====
-State Explosion phenomenon is still the major hurdle in model checking of large system implementations. Therefore complete search techniques such as partial order reduction are not compelling anymore. Instead, heuristics for moving the search towards more relevant states are totally welcome. The heuristic can be applied in different parts of the model checking process, such as modeling the environment, exploration algorithm, and initial state.
-Storing/Loading of states in large software systems is very expensive, both in terms of time and memory. The stateful approach used to be necessary to achieve complete search. Having feasibility been prioritized over completeness, stateless approach sounds more realistic for quick exploration in state space.
-Currently, modeling the environment is achieved mostly by heuristics. Precise definition of the process as well as tools for automation of this process would make the effective model checking of software systems one step closer to reality.
-Time and random values are still two big problems in model checking. Although application-specific solutions have been proposed, there is still no approach to force the developers to use them. Perhaps, the future programming languages can be **model checking-aware** in the sense that they force the developers to use only the mechanisms that are already instrumented to be used in model checkers.