Infeasible Path Detection Research Articles

Automatic detection of infeasible paths in large-scale program based on program summaries

The existence of infeasible paths in a program reduces the coverage of test cases and causes a waste of valuable testing resources. Detecting infeasible paths allows for focusing testing resources on feasible paths. This paper introduces a method for detecting infeasible paths based on program summaries. Our proposed method partitions the program into sequential statements, conditional statements and loop statements, and automatically generates statement summaries and function summaries. It analyzes the summaries to extract the path constraints and determines the feasibility of paths. We implemented a detection tool named DTSIP based on this method, and conducted experiments using a set of benchmark programs and open source projects. The results confirm the effectiveness of our method in detecting infeasible paths. It can detect both intraprocedural and interprocedural infeasible paths, demonstrating its broad applicability. Our method overcomes challenges associated with analyzing complex paths, achieving efficient feasibility determination while reducing processing time.

Infeasible Path Detection Based on Code Pattern and Backward Symbolic Execution

This paper sets out to reveal the relationship between code pattern and infeasible paths and gives advices to the selection of infeasible path detection techniques. Lots of program paths are proved to be infeasible, which leads to imprecision and low efficiency of program analysis. Detection of infeasible paths is required in many areas of software engineering including test coverage analysis, test case generation, and security vulnerability analysis. The immediate cause of path infeasibility is the contradiction of path constraints, whose distribution will affect the performance of different program analysis techniques. But there is a lack of research on the distribution of contradict constraints currently. We propose a code pattern based on the empirical study of infeasible paths; the statistical result proves the correlation of the pattern with contradict constraints. We then develop a path feasibility detection method based on backward symbolic execution. Performance of the proposed technique is evaluated from two aspects: the efficiency of detecting infeasibility paths for specific program element and the improvement of applying the technique on code coverage testing.

An Approach for Detecting Infeasible Paths Based on a SMT Solver

Software testing is an important means to ensure software quality. Testers need to ensure that every component of the software is tested correctly to achieve high coverage, such as path coverage, decision coverage, and branch coverage. An infeasible path is a path that cannot be traversed by any test cases. The existence of infeasible paths can waste test resources; therefore, detection of infeasible paths are necessary before path testing. This paper presents a static method for the detecting infeasible paths that is based on a satisfiability modulo theory (SMT) solver. First, the proposed method generates a sub-path set and converts the feasibility issues into inequalities. Second, a constraint solver is used to solve the inequalities and, then, the sub-paths are divided into two categories: infeasible sub-paths and undetermined sub-paths. The paths that were expanded from the latter will be tested again to determine their feasibility. Finally, the feasibility of all paths is detected. Most of the detection works are done on the sub-path set; therefore, our method provides an effective solution to the path-explosion problem. The experimental results showed that the proposed method can detect infeasible paths more accurately and effectively than most existing methods.

Detection of Infeasible Paths in Software Testing using UML Application to Gold Vending Machine

Detection of Infeasible Paths in Software Testing using UML Application to Gold Vending Machine

Performance debugging of Esterel specifications

Synchronous languages like Esterel have been widely adopted for designing reactive systems in safety-critical domains such as avionics. Specifications written in Esterel are based on the underlying synchrony hypothesis, which needs to be validated when Esterel specifications get compiled to real implementations (such as C code). In this work, we present a model-driven and architecture-aware timing analysis framework for C code generated from Esterel and executed on general-purpose processors. By integrating model-level information into the traditional timing analysis, we can efficiently compute accurate time estimates via systematically eliminating a large number of infeasible paths in the generated code. Experimental results show that with our proposed intermediate representation level infeasible path analysis in the model compilation, we obtain up to 16.1 % tighter WCET estimates compared to the traditional assembly code level infeasible path detection with substantially less analysis time. Furthermore, by maintaining the traceability links between Esterel specifications and the generated C code, we are able to map the time-critical computations at the C-level back to the Esterel-level.

Automatic detection of infeasible paths in software testing

A challenging problem in path-oriented test data generation is the presence of infeasible paths. Timely detecting these infeasible paths cannot only save test resources but also improve test efficiency. A popular method of detecting infeasible paths is to determine branch correlations, which is a difficult task and usually cannot be done timely and exactly. In this study, the authors propose a method of automatically determining the branch correlations of different conditional statements, therefore detecting infeasible paths. First, some theorems are given to determine branch correlations based on the probabilities of the conditional distribution corresponding to different branches' outcome (i.e. true or false); then, the maximum likelihood estimation is employed to obtain the values of these probabilities; finally, infeasible paths are detected according to branch correlations. The authors apply the proposed method in some typical programs, and the results show that the proposed method can accurately detect infeasible paths. The achievement provides an effective and automatic method of detecting infeasible paths, which is significant in improving the efficiency of software testing.

Heuristics-based infeasible path detection for dynamic test data generation

Automated test data generation plays an important part in reducing the cost and increasing the reliability of software testing. However, a challenging problem in path-oriented test data generation is the existence of infeasible program paths, where considerable effort may be wasted in trying to generate input data to traverse the paths. In this paper, we propose a heuristics-based approach to infeasible path detection for dynamic test data generation. Our approach is based on the observation that many infeasible program paths exhibit some common properties. Through realizing these properties in execution traces collected during the test data generation process, infeasible paths can be detected early with high accuracy. Our experiments show that the proposed approach efficiently detects most of the infeasible paths with an average precision of 96.02% and a recall of 100% of all the cases.

Program slicing based on data-flow computation of path dependence flow graphs

Program slicing is a technique to extract from a program a set of statements (referred to as a slice) that have certain properties; such techniques are widely used, for example, for simplification and maintenance of software. This paper proposes a particular method to generate a slice that is formally represented by using a path dependence flow graph (PDFG), namely, by means of interpretive execution of the PDFG based on data-flow computation (1). Since the proposed method is a generalization of the conventional method of interpretive execution of PDFG based on data-flow computation, the same scheme of computation control can be employed as is used in other techniques of PDFG analysis, for example, infeasible path detection (2). The slicing algorithm is specified for nodes in the form of local rules. Therefore, by partially modifying those rules, new varieties of slices can be easily obtained, for example, more precise slices (3). During firing control in data-flow computation, executable nodes are found, and slice is generated simply by consecutive execution of rules at those nodes, which implies simple computation control (4). Since control of slicing can be distributed among nodes, parallel processing is possible (5). A generalized method that offers generation of slices not obtainable by conventional methods is proposed. © 1998 Scripta Technica, Syst Comp Jpn, 28(11): 22–33, 1997

An investigation of the applicability of data usage analysis

An investigation into the application of data usage analysis to detect faults in large data processing programs was performed. This investigation was done to gain insight into the applicability of data usage analysis. The method uses stylized comments, called data usage comments. A prototype tool, DataCheck, was developed which incorporated a limited form of infeasible path detection. The investigation showed that data usage analysis is effective in the detection of a general class of persistent data usage faults in large programs and that the type of infeasible path detection implemented in DataCheck is sufficient to eliminate most spurious data usage error messages. The difference between data usage analysis and other technologies like variable reference analysis are described.