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M. Soltani
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Crash reproduction approaches help developers during debugging by generating a test case that reproduces a given crash. Several solutions have been proposed to automate this task. However, the proposed solutions have been evaluated on a limited number of projects, making comparison difficult. In this paper, we enhance this line of research by proposing JCrashPack, an extensible benchmark for Java crash reproduction, together with ExRunner, a tool to simply and systematically run evaluations. JCrashPack contains 200 stack traces from various Java projects, including industrial open source ones, on which we run an extensive evaluation of EvoCrash, the state-of-the-art tool for search-based crash reproduction. EvoCrash successfully reproduced 43% of the crashes. Furthermore, we observed that reproducing NullPointerException, IllegalArgumentException, and IllegalStateException is relatively easier than reproducing ClassCastException, ArrayIndexOutOfBoundsException and StringIndexOutOfBoundsException. Our results include a detailed manual analysis of EvoCrash outputs, from which we derive 14 current challenges for crash reproduction, among which the generation of input data and the handling of abstract and anonymous classes are the most frequents. Finally, based on those challenges, we discuss future research directions for search-based crash reproduction for Java.
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Crash reproduction approaches help developers during debugging by generating a test case that reproduces a given crash. Several solutions have been proposed to automate this task. However, the proposed solutions have been evaluated on a limited number of projects, making comparison difficult. In this paper, we enhance this line of research by proposing JCrashPack, an extensible benchmark for Java crash reproduction, together with ExRunner, a tool to simply and systematically run evaluations. JCrashPack contains 200 stack traces from various Java projects, including industrial open source ones, on which we run an extensive evaluation of EvoCrash, the state-of-the-art tool for search-based crash reproduction. EvoCrash successfully reproduced 43% of the crashes. Furthermore, we observed that reproducing NullPointerException, IllegalArgumentException, and IllegalStateException is relatively easier than reproducing ClassCastException, ArrayIndexOutOfBoundsException and StringIndexOutOfBoundsException. Our results include a detailed manual analysis of EvoCrash outputs, from which we derive 14 current challenges for crash reproduction, among which the generation of input data and the handling of abstract and anonymous classes are the most frequents. Finally, based on those challenges, we discuss future research directions for search-based crash reproduction for Java.
Software systems fail. These failures are often reported to issue tracking systems, where they are prioritized and assigned to responsible developers to be investigated. When developers debug software, they need to reproduce the reported failure in order to verify whether their fix actually prevents the failure from happening again. Since manually reproducing each failure could be a complex task, several automated techniques have been proposed to tackle this problem. Despite showing advancements in this area, the proposed techniques showed various types of limitations. In this paper, we present EvoCrash, a new approach to automated crash reproduction based on a novel evolutionary algorithm, called Guided Genetic Algorithm (GGA). We report on our empirical study on using EvoCrash to reproduce 54 real-world crashes, as well as the results of a controlled experiment, involving human participants, to assess the impact of EvoCrash tests in debugging. Based on our results, EvoCrash outperforms state-of-the-art techniques in crash reproduction and uncovers failures that are undetected by classical coverage-based unit test generation tools. In addition, we observed that using EvoCrash helps developers provide fixes more often and take less time when debugging, compared to developers debugging and fixing code without using EvoCrash tests.
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Software systems fail. These failures are often reported to issue tracking systems, where they are prioritized and assigned to responsible developers to be investigated. When developers debug software, they need to reproduce the reported failure in order to verify whether their fix actually prevents the failure from happening again. Since manually reproducing each failure could be a complex task, several automated techniques have been proposed to tackle this problem. Despite showing advancements in this area, the proposed techniques showed various types of limitations. In this paper, we present EvoCrash, a new approach to automated crash reproduction based on a novel evolutionary algorithm, called Guided Genetic Algorithm (GGA). We report on our empirical study on using EvoCrash to reproduce 54 real-world crashes, as well as the results of a controlled experiment, involving human participants, to assess the impact of EvoCrash tests in debugging. Based on our results, EvoCrash outperforms state-of-the-art techniques in crash reproduction and uncovers failures that are undetected by classical coverage-based unit test generation tools. In addition, we observed that using EvoCrash helps developers provide fixes more often and take less time when debugging, compared to developers debugging and fixing code without using EvoCrash tests.
Conference paper
(2018)
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Mozhan Soltani, Pouria Derakhshanfar, Annibale Panichella, Xavier Devroey, Andy Zaidman, Arie van Deursen
EvoCrash is a recent search-based approach to generate a test case that reproduces reported crashes. The search is guided by a fitness function that uses a weighted sum scalarization to combine three different heuristics: (i) code coverage, (ii) crash coverage and (iii) stack trace similarity. In this study, we propose and investigate two alternatives to the weighted sum scalarization: (i) the simple sum scalarization and (ii) the multi-objectivization, which decomposes the fitness function into several optimization objectives as an attempt to increase test case diversity. We implemented the three alternative optimizations as an extension of EvoSuite, a popular search-based unit test generator, and applied them on 33 real-world crashes. Our results indicate that for complex crashes the weighted sum reduces the test case generation time, compared to the simple sum, while for simpler crashes the effect is the opposite. Similarly, for complex crashes, multi-objectivization reduces test generation time compared to optimizing with the weighted sum; we also observe one crash that can be replicated only by multi-objectivization. Through our manual analysis, we found out that when optimizing the original weighted function gets trapped in local optima, optimization for decomposed objectives improves the search for crash reproduction. Generally, while multi-objectivization is under-explored, our results are promising and encourage further investigations of the approach.
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EvoCrash is a recent search-based approach to generate a test case that reproduces reported crashes. The search is guided by a fitness function that uses a weighted sum scalarization to combine three different heuristics: (i) code coverage, (ii) crash coverage and (iii) stack trace similarity. In this study, we propose and investigate two alternatives to the weighted sum scalarization: (i) the simple sum scalarization and (ii) the multi-objectivization, which decomposes the fitness function into several optimization objectives as an attempt to increase test case diversity. We implemented the three alternative optimizations as an extension of EvoSuite, a popular search-based unit test generator, and applied them on 33 real-world crashes. Our results indicate that for complex crashes the weighted sum reduces the test case generation time, compared to the simple sum, while for simpler crashes the effect is the opposite. Similarly, for complex crashes, multi-objectivization reduces test generation time compared to optimizing with the weighted sum; we also observe one crash that can be replicated only by multi-objectivization. Through our manual analysis, we found out that when optimizing the original weighted function gets trapped in local optima, optimization for decomposed objectives improves the search for crash reproduction. Generally, while multi-objectivization is under-explored, our results are promising and encourage further investigations of the approach.
Conference paper
(2018)
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Jeroen Castelein, Maurício Aniche, Mozhan Soltani, Annibale Panichella, Arie van Deursen
Database-centric systems strongly rely on SQL queries to manage and manipulate their data. These SQL commands can range from very simple selections to queries that involve several tables, subqueries, and grouping operations. And, as with any important piece of code, developers should properly test SQL queries. In order to completely test a SQL query, developers need to create test data that exercise all possible coverage targets in a query, e.g., JOINs and WHERE predicates. And indeed, this task can be challenging and time-consuming for complex queries. Previous studies have modeled the problem of generating test data as a constraint satisfaction problem and, with the help of SAT solvers, generate the required data. However, such approaches have strong limitations, such as partial support for queries with JOINs, subqueries, and strings (which are commonly used in SQL queries). In this paper, we model test data generation for SQL queries as a search-based problem. Then, we devise and evaluate three different approaches based on random search, biased random search, and genetic algorithms (GAs). The GA, in particular, uses a fitness function based on information extracted from the physical query plan of a database engine as search guidance. We then evaluate each approach in 2,135 queries extracted from three open source software and one industrial software system. Our results show that GA is able to completely cover 98.6% of all queries in the dataset, requiring only a few seconds for each query. Moreover, it does not suffer from the limitations affecting state-of-the art techniques.
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Database-centric systems strongly rely on SQL queries to manage and manipulate their data. These SQL commands can range from very simple selections to queries that involve several tables, subqueries, and grouping operations. And, as with any important piece of code, developers should properly test SQL queries. In order to completely test a SQL query, developers need to create test data that exercise all possible coverage targets in a query, e.g., JOINs and WHERE predicates. And indeed, this task can be challenging and time-consuming for complex queries. Previous studies have modeled the problem of generating test data as a constraint satisfaction problem and, with the help of SAT solvers, generate the required data. However, such approaches have strong limitations, such as partial support for queries with JOINs, subqueries, and strings (which are commonly used in SQL queries). In this paper, we model test data generation for SQL queries as a search-based problem. Then, we devise and evaluate three different approaches based on random search, biased random search, and genetic algorithms (GAs). The GA, in particular, uses a fitness function based on information extracted from the physical query plan of a database engine as search guidance. We then evaluate each approach in 2,135 queries extracted from three open source software and one industrial software system. Our results show that GA is able to completely cover 98.6% of all queries in the dataset, requiring only a few seconds for each query. Moreover, it does not suffer from the limitations affecting state-of-the art techniques.
To reduce the effort developers have to make for crash debugging, researchers have proposed several solutions for automatic failure reproduction. Recent advances proposed the use of symbolic execution, mutation analysis, and directed model checking as underling techniques for post-failure analysis of crash stack traces. However, existing approaches still cannot reproduce many real-world crashes due to such limitations as environment dependencies, path explosion, and time complexity. To address these challenges, we present EvoCrash, a post-failure approach which uses a novel Guided Genetic Algorithm (GGA) to cope with the large search space characterizing real-world software programs. Our empirical study on three open-source systems shows that EvoCrash can replicate 41 (82%) of real-world crashes, 34 (89%) of which are useful reproductions for debugging purposes, outperforming the state-of-the-art in crash replication.
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To reduce the effort developers have to make for crash debugging, researchers have proposed several solutions for automatic failure reproduction. Recent advances proposed the use of symbolic execution, mutation analysis, and directed model checking as underling techniques for post-failure analysis of crash stack traces. However, existing approaches still cannot reproduce many real-world crashes due to such limitations as environment dependencies, path explosion, and time complexity. To address these challenges, we present EvoCrash, a post-failure approach which uses a novel Guided Genetic Algorithm (GGA) to cope with the large search space characterizing real-world software programs. Our empirical study on three open-source systems shows that EvoCrash can replicate 41 (82%) of real-world crashes, 34 (89%) of which are useful reproductions for debugging purposes, outperforming the state-of-the-art in crash replication.
Manual crash reproduction is a labor-intensive and time-consuming task. Therefore, several solutions have been proposed in literature for automatic crash reproduction, including generating unit tests via symbolic execution and mutation analysis. However, various limitations adversely affect the capabilities of the existing solutions in covering a wider range of crashes because generating helpful tests that trigger specific execution paths is particularly challenging. In this paper, we propose a new solution for automatic crash reproduction based on evolutionary unit test generation techniques. The proposed solution exploits crash data from collected stack traces to guide search-based algorithms toward the generation of unit test cases that can reproduce the original crashes. Results from our preliminary study on real crashes from Apache Commons libraries show that our solution can successfully reproduce crashes which are not reproducible by two other state-of-art techniques.
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Manual crash reproduction is a labor-intensive and time-consuming task. Therefore, several solutions have been proposed in literature for automatic crash reproduction, including generating unit tests via symbolic execution and mutation analysis. However, various limitations adversely affect the capabilities of the existing solutions in covering a wider range of crashes because generating helpful tests that trigger specific execution paths is particularly challenging. In this paper, we propose a new solution for automatic crash reproduction based on evolutionary unit test generation techniques. The proposed solution exploits crash data from collected stack traces to guide search-based algorithms toward the generation of unit test cases that can reproduce the original crashes. Results from our preliminary study on real crashes from Apache Commons libraries show that our solution can successfully reproduce crashes which are not reproducible by two other state-of-art techniques.