{"title":"ISSTA '22: 31st ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, South Korea, July 18 - 22, 2022","authors":"","doi":"10.1145/3533767","DOIUrl":"https://doi.org/10.1145/3533767","url":null,"abstract":"","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85524541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ISSTA '21: 30th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, Denmark, July 11-17, 2021","authors":"","doi":"10.1145/3460319","DOIUrl":"https://doi.org/10.1145/3460319","url":null,"abstract":"","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82280269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Over the last few years, MQTT applications have been widely exposed to vulnerabilities because of their weak protocol implementations. For our preliminary research, we conducted background studies to: (1) determine the main cause of vulnerabilities in MQTT applications; and (2) analyze existing MQTT-based testing frameworks. Our preliminary results confirm that MQTT is most susceptible to malformed packets, and its existing testing frameworks are based on blackbox fuzzing, meaning vulnerabilities are difficult and time-consuming to find. Thus, the aim of my research is to study and develop effective fuzzing strategies for the MQTT protocol, thereby contributing to the development of more robust MQTT applications in IoT and Smart Cities.
{"title":"Program-aware fuzzing for MQTT applications","authors":"Luis Gustavo Araujo Rodriguez, D. Batista","doi":"10.1145/3395363.3402645","DOIUrl":"https://doi.org/10.1145/3395363.3402645","url":null,"abstract":"Over the last few years, MQTT applications have been widely exposed to vulnerabilities because of their weak protocol implementations. For our preliminary research, we conducted background studies to: (1) determine the main cause of vulnerabilities in MQTT applications; and (2) analyze existing MQTT-based testing frameworks. Our preliminary results confirm that MQTT is most susceptible to malformed packets, and its existing testing frameworks are based on blackbox fuzzing, meaning vulnerabilities are difficult and time-consuming to find. Thus, the aim of my research is to study and develop effective fuzzing strategies for the MQTT protocol, thereby contributing to the development of more robust MQTT applications in IoT and Smart Cities.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"292 ","pages":"582-586"},"PeriodicalIF":0.0,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91445281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Software testing activity is imperative to improve software quality. However, finding a set of test cases satisfies a given test criterion, is not a trivial task because the overall input domain is very large, and different test sets can be derived, with different effectiveness. In the context of structural testing, the non-executability is a feature present in most programs, increasing cost and effort of testing activity. When concurrent programs are tested, new challenges arise, mainly related to the non-determinism. Non-determinism can result in different possible test outputs for the same test input, which makes the problem of non-executability more complex, requiring treatment. In this sense, our project intends to define an approach to support automatic identification of infeasible testing requirements. Hence, this proposal aims to identify properties which cause infeasible testing requirements and automate their application. Due to complexity of the problem, we will apply search-based algorithms in the automation of concurrent and sequential programs treatment.
{"title":"Automatic support for the identification of infeasible testing requirements","authors":"João Choma Neto","doi":"10.1145/3395363.3402646","DOIUrl":"https://doi.org/10.1145/3395363.3402646","url":null,"abstract":"Software testing activity is imperative to improve software quality. However, finding a set of test cases satisfies a given test criterion, is not a trivial task because the overall input domain is very large, and different test sets can be derived, with different effectiveness. In the context of structural testing, the non-executability is a feature present in most programs, increasing cost and effort of testing activity. When concurrent programs are tested, new challenges arise, mainly related to the non-determinism. Non-determinism can result in different possible test outputs for the same test input, which makes the problem of non-executability more complex, requiring treatment. In this sense, our project intends to define an approach to support automatic identification of infeasible testing requirements. Hence, this proposal aims to identify properties which cause infeasible testing requirements and automate their application. Due to complexity of the problem, we will apply search-based algorithms in the automation of concurrent and sequential programs treatment.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"62 1","pages":"587-591"},"PeriodicalIF":0.0,"publicationDate":"2020-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80820787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"ISSTA '20: 29th ACM SIGSOFT International Symposium on Software Testing and Analysis, Virtual Event, USA, July 18-22, 2020","authors":"","doi":"10.1145/3395363","DOIUrl":"https://doi.org/10.1145/3395363","url":null,"abstract":"","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88351030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is particularly challenging to defend common distributed systems against security vulnerabilities because of the complexity and their large sizes. However, traditional solutions, that attack the information flow security problem, often fail for large, complex real-world distributed systems due to scalability problems. The problem would be even exacerbated for the online defense of continuously-running systems. My proposed research consists of three connected themes. First, I have developed metrics to help users understand and analyze the security characteristics of distributed systems at runtime in relation to their coupling measures. Then, I have also developed a highly scalable, cost-effective dynamic information flow analysis approach for distributed systems. It can detect implicit dependencies and find real security vulnerabilities in industrial distributed systems with practical portability and scalability. In order to thoroughly solve the scalability problem in general scenarios, I am developing a self-adaptive dynamic dependency analysis framework to monitor security issues during continuous running. In this proposal, I outline the three projects in a related manner as to how they consistently target the central objective of my thesis research.
{"title":"Towards scalable defense of information flow security for distributed systems","authors":"Xiaoqin Fu","doi":"10.1145/3293882.3338988","DOIUrl":"https://doi.org/10.1145/3293882.3338988","url":null,"abstract":"It is particularly challenging to defend common distributed systems against security vulnerabilities because of the complexity and their large sizes. However, traditional solutions, that attack the information flow security problem, often fail for large, complex real-world distributed systems due to scalability problems. The problem would be even exacerbated for the online defense of continuously-running systems. My proposed research consists of three connected themes. First, I have developed metrics to help users understand and analyze the security characteristics of distributed systems at runtime in relation to their coupling measures. Then, I have also developed a highly scalable, cost-effective dynamic information flow analysis approach for distributed systems. It can detect implicit dependencies and find real security vulnerabilities in industrial distributed systems with practical portability and scalability. In order to thoroughly solve the scalability problem in general scenarios, I am developing a self-adaptive dynamic dependency analysis framework to monitor security issues during continuous running. In this proposal, I outline the three projects in a related manner as to how they consistently target the central objective of my thesis research.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76474177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing program repair systems modify a buggy program so that the modified program passes given tests. The repaired program may not satisfy even the most basic notion of correctness, namely crash-freedom. In other words, repair tools might generate patches which over-fit the test data driving the repair, and the automatically repaired programs may even introduce crashes or vulnerabilities. We propose an integrated approach for detecting and discarding crashing patches. Our approach fuses test and patch generation into a single process, in which patches are generated with the objective of passing existing tests, and new tests are generated with the objective of filtering out over-fitted patches by distinguishing candidate patches in terms of behavior. We use crash-freedom as the oracle to discard patch candidates which crash on the new tests. In its core, our approach defines a grey-box fuzzing strategy that gives higher priority to new tests that separate patches behaving equivalently on existing tests. This test generation strategy identifies semantic differences between patch candidates, and reduces over-fitting in program repair. We evaluated our approach on real-world vulnerabilities and open-source subjects from the Google OSS-Fuzz infrastructure. We found that our tool Fix2Fit (implementing patch space directed test generation), produces crash-avoiding patches. While we do not give formal guarantees about crash-freedom, cross-validation with fuzzing tools and their sanitizers provides greater confidence about the crash-freedom of our suggested patches.
{"title":"Crash-avoiding program repair","authors":"Xiang Gao, Sergey Mechtaev, Abhik Roychoudhury","doi":"10.1145/3293882.3330558","DOIUrl":"https://doi.org/10.1145/3293882.3330558","url":null,"abstract":"Existing program repair systems modify a buggy program so that the modified program passes given tests. The repaired program may not satisfy even the most basic notion of correctness, namely crash-freedom. In other words, repair tools might generate patches which over-fit the test data driving the repair, and the automatically repaired programs may even introduce crashes or vulnerabilities. We propose an integrated approach for detecting and discarding crashing patches. Our approach fuses test and patch generation into a single process, in which patches are generated with the objective of passing existing tests, and new tests are generated with the objective of filtering out over-fitted patches by distinguishing candidate patches in terms of behavior. We use crash-freedom as the oracle to discard patch candidates which crash on the new tests. In its core, our approach defines a grey-box fuzzing strategy that gives higher priority to new tests that separate patches behaving equivalently on existing tests. This test generation strategy identifies semantic differences between patch candidates, and reduces over-fitting in program repair. We evaluated our approach on real-world vulnerabilities and open-source subjects from the Google OSS-Fuzz infrastructure. We found that our tool Fix2Fit (implementing patch space directed test generation), produces crash-avoiding patches. While we do not give formal guarantees about crash-freedom, cross-validation with fuzzing tools and their sanitizers provides greater confidence about the crash-freedom of our suggested patches.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79977555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Learning-based fault localization has been intensively studied recently. Prior studies have shown that traditional Learning-to-Rank techniques can help precisely diagnose fault locations using various dimensions of fault-diagnosis features, such as suspiciousness values computed by various off-the-shelf fault localization techniques. However, with the increasing dimensions of features considered by advanced fault localization techniques, it can be quite challenging for the traditional Learning-to-Rank algorithms to automatically identify effective existing/latent features. In this work, we propose DeepFL, a deep learning approach to automatically learn the most effective existing/latent features for precise fault localization. Although the approach is general, in this work, we collect various suspiciousness-value-based, fault-proneness-based and textual-similarity-based features from the fault localization, defect prediction and information retrieval areas, respectively. DeepFL has been studied on 395 real bugs from the widely used Defects4J benchmark. The experimental results show DeepFL can significantly outperform state-of-the-art TraPT/FLUCCS (e.g., localizing 50+ more faults within Top-1). We also investigate the impacts of deep model configurations (e.g., loss functions and epoch settings) and features. Furthermore, DeepFL is also surprisingly effective for cross-project prediction.
{"title":"DeepFL: integrating multiple fault diagnosis dimensions for deep fault localization","authors":"Xia Li, Wei Li, Yuqun Zhang, Lingming Zhang","doi":"10.1145/3293882.3330574","DOIUrl":"https://doi.org/10.1145/3293882.3330574","url":null,"abstract":"Learning-based fault localization has been intensively studied recently. Prior studies have shown that traditional Learning-to-Rank techniques can help precisely diagnose fault locations using various dimensions of fault-diagnosis features, such as suspiciousness values computed by various off-the-shelf fault localization techniques. However, with the increasing dimensions of features considered by advanced fault localization techniques, it can be quite challenging for the traditional Learning-to-Rank algorithms to automatically identify effective existing/latent features. In this work, we propose DeepFL, a deep learning approach to automatically learn the most effective existing/latent features for precise fault localization. Although the approach is general, in this work, we collect various suspiciousness-value-based, fault-proneness-based and textual-similarity-based features from the fault localization, defect prediction and information retrieval areas, respectively. DeepFL has been studied on 395 real bugs from the widely used Defects4J benchmark. The experimental results show DeepFL can significantly outperform state-of-the-art TraPT/FLUCCS (e.g., localizing 50+ more faults within Top-1). We also investigate the impacts of deep model configurations (e.g., loss functions and epoch settings) and features. Furthermore, DeepFL is also surprisingly effective for cross-project prediction.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80797831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yiling Lou, Junjie Chen, Lingming Zhang, Dan Hao, Lu Zhang
Build systems are essential for modern software development and maintenance since they are widely used to transform source code artifacts into executable software. Previous work shows that build systems break frequently during software evolution. Therefore, automated build-fixing techniques are in huge demand. In this paper we target a mainstream build system, Gradle, which has become the most widely used build system for Java projects in the open-source community (e.g., GitHub). HireBuild, state-of-the-art build-fixing tool for Gradle, has been recently proposed to fix Gradle build failures via mining the history of prior fixes. Although HireBuild has been shown to be effective for fixing real-world Gradle build failures, it was evaluated on only a limited set of build failures, and largely depends on the quality/availability of historical fix information. To investigate the efficacy and limitations of the history-driven build fix, we first construct a new and large build failure dataset from Top-1000 GitHub projects. Then, we evaluate HireBuild on the extended dataset both quantitatively and qualitatively. Inspired by the findings of the study, we propose a simplistic new technique that generates potential patches via searching from the present project under test and external resources rather than the historical fix information. According to our experimental results, the simplistic approach based on present information successfully fixes 2X more reproducible build failures than the state-of-art HireBuild based on historical fix information. Furthermore, our results also reveal various findings/guidelines for future advanced build failure fixing.
{"title":"History-driven build failure fixing: how far are we?","authors":"Yiling Lou, Junjie Chen, Lingming Zhang, Dan Hao, Lu Zhang","doi":"10.1145/3293882.3330578","DOIUrl":"https://doi.org/10.1145/3293882.3330578","url":null,"abstract":"Build systems are essential for modern software development and maintenance since they are widely used to transform source code artifacts into executable software. Previous work shows that build systems break frequently during software evolution. Therefore, automated build-fixing techniques are in huge demand. In this paper we target a mainstream build system, Gradle, which has become the most widely used build system for Java projects in the open-source community (e.g., GitHub). HireBuild, state-of-the-art build-fixing tool for Gradle, has been recently proposed to fix Gradle build failures via mining the history of prior fixes. Although HireBuild has been shown to be effective for fixing real-world Gradle build failures, it was evaluated on only a limited set of build failures, and largely depends on the quality/availability of historical fix information. To investigate the efficacy and limitations of the history-driven build fix, we first construct a new and large build failure dataset from Top-1000 GitHub projects. Then, we evaluate HireBuild on the extended dataset both quantitatively and qualitatively. Inspired by the findings of the study, we propose a simplistic new technique that generates potential patches via searching from the present project under test and external resources rather than the historical fix information. According to our experimental results, the simplistic approach based on present information successfully fixes 2X more reproducible build failures than the state-of-art HireBuild based on historical fix information. Furthermore, our results also reveal various findings/guidelines for future advanced build failure fixing.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79861004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present JQF, a platform for performing coverage-guided fuzz testing in Java. JQF is designed both for practitioners, who wish to find bugs in Java programs, as well as for researchers, who wish to implement new fuzzing algorithms. Practitioners write QuickCheck-style test methods that take inputs as formal parameters. JQF instruments the test program's bytecode and continuously executes tests using inputs that are generated in a coverage-guided fuzzing loop. JQF's input-generation mechanism is extensible. Researchers can implement custom fuzzing algorithms by extending JQF's Guidance interface. A Guidance instance responds to code coverage events generated during the execution of a test case, such as function calls and conditional jumps, and provides the next input. We describe several guidances that currently ship with JQF, such as: semantic fuzzing with Zest, binary fuzzing with AFL, and complexity fuzzing with PerfFuzz. JQF is a mature tool that is open-source and publicly available. At the time of writing, JQF has been successful in discovering 42 previously unknown bugs in widely used open-source software such as OpenJDK, Apache Commons, and the Google Closure Compiler.
{"title":"JQF: coverage-guided property-based testing in Java","authors":"Rohan Padhye, Caroline Lemieux, Koushik Sen","doi":"10.1145/3293882.3339002","DOIUrl":"https://doi.org/10.1145/3293882.3339002","url":null,"abstract":"We present JQF, a platform for performing coverage-guided fuzz testing in Java. JQF is designed both for practitioners, who wish to find bugs in Java programs, as well as for researchers, who wish to implement new fuzzing algorithms. Practitioners write QuickCheck-style test methods that take inputs as formal parameters. JQF instruments the test program's bytecode and continuously executes tests using inputs that are generated in a coverage-guided fuzzing loop. JQF's input-generation mechanism is extensible. Researchers can implement custom fuzzing algorithms by extending JQF's Guidance interface. A Guidance instance responds to code coverage events generated during the execution of a test case, such as function calls and conditional jumps, and provides the next input. We describe several guidances that currently ship with JQF, such as: semantic fuzzing with Zest, binary fuzzing with AFL, and complexity fuzzing with PerfFuzz. JQF is a mature tool that is open-source and publicly available. At the time of writing, JQF has been successful in discovering 42 previously unknown bugs in widely used open-source software such as OpenJDK, Apache Commons, and the Google Closure Compiler.","PeriodicalId":20624,"journal":{"name":"Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis","volume":"169 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76782236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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