Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00061
Changlin Liu
Mobile apps have been an integral part in our daily life. As these apps become more complex, it is critical to provide automated analysis techniques to ensure the correctness, security, and performance of these apps. A key component for these automated analysis techniques is to create a graphical user interface (GUI) model of an app, i.e., a window transition graph (WTG), that models windows and transitions among the windows. While existing work has provided both static and dynamic analysis to build the WTG for an app, the constructed WTG misses many transitions or contains many infeasible transitions due to the coverage issues of dynamic analysis and over-approximation of the static analysis. We propose ProMal, a "tribrid" analysis that synergistically combines static analysis, dynamic analysis, and machine learning to construct a precise WTG. Specifically, ProMal first applies static analysis to build a static WTG, and then applies dynamic analysis to verify the transitions in the static WTG. For the unverified transitions, ProMal further provides machine learning techniques that leverage runtime information (i.e., screenshots, UI layouts, and text information) to predict whether they are feasible transitions. Our evaluations on 40 real-world apps demonstrate the superiority of ProMal in building WTGs over static analysis, dynamic analysis, and machine learning techniques when they are applied separately.
{"title":"ProMal: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning","authors":"Changlin Liu","doi":"10.1109/ICSE-Companion52605.2021.00061","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00061","url":null,"abstract":"Mobile apps have been an integral part in our daily life. As these apps become more complex, it is critical to provide automated analysis techniques to ensure the correctness, security, and performance of these apps. A key component for these automated analysis techniques is to create a graphical user interface (GUI) model of an app, i.e., a window transition graph (WTG), that models windows and transitions among the windows. While existing work has provided both static and dynamic analysis to build the WTG for an app, the constructed WTG misses many transitions or contains many infeasible transitions due to the coverage issues of dynamic analysis and over-approximation of the static analysis. We propose ProMal, a \"tribrid\" analysis that synergistically combines static analysis, dynamic analysis, and machine learning to construct a precise WTG. Specifically, ProMal first applies static analysis to build a static WTG, and then applies dynamic analysis to verify the transitions in the static WTG. For the unverified transitions, ProMal further provides machine learning techniques that leverage runtime information (i.e., screenshots, UI layouts, and text information) to predict whether they are feasible transitions. Our evaluations on 40 real-world apps demonstrate the superiority of ProMal in building WTGs over static analysis, dynamic analysis, and machine learning techniques when they are applied separately.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129226409","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00100
F. Grund, S. A. Chowdhury, N. Bradley, Braxton Hall, Reid Holmes
Being able to accurately understand how source code evolved is fundamentally important for both software engineers and researchers. Our ICSE 2021 Research Paper CodeShovel: Constructing Method-Level Source Code Histories describes a novel approach for quickly uncovering these method histories. The approach, codified in the CodeShovel tool, is available for researchers and practitioners alike to use and extend. It is available both as a public web service that can be used interactively or through a REST API and as a stand-alone Java component. This document details how to install and use CodeShovel, although all pertinent details are available online enabling CodeShovel to be reused as desired.
{"title":"CodeShovel: A Reusable and Available Tool for Extracting Source Code Histories","authors":"F. Grund, S. A. Chowdhury, N. Bradley, Braxton Hall, Reid Holmes","doi":"10.1109/ICSE-Companion52605.2021.00100","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00100","url":null,"abstract":"Being able to accurately understand how source code evolved is fundamentally important for both software engineers and researchers. Our ICSE 2021 Research Paper CodeShovel: Constructing Method-Level Source Code Histories describes a novel approach for quickly uncovering these method histories. The approach, codified in the CodeShovel tool, is available for researchers and practitioners alike to use and extend. It is available both as a public web service that can be used interactively or through a REST API and as a stand-alone Java component. This document details how to install and use CodeShovel, although all pertinent details are available online enabling CodeShovel to be reused as desired.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114039146","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}
Pub Date : 2021-05-01DOI: 10.1109/ICSE-Companion52605.2021.00068
David Shriver, Sebastian G. Elbaum, Matthew B. Dwyer
We present an artifact to accompany Reducing DNN Properties to Enable Falsification with Adversarial Attacks which includes the DNNF tool, data and scripts to facilitate the replication of its study. The artifact is both reusable and available. DNNF is available on Github, and we provide an artifact to reproduce our study as a VirtualBox virtual machine image. Full replication of the study requires 64GB of memory and 8 CPU cores. Users should know how to use VirtualBox, as well as have basic knowledge of the bash shell.
{"title":"Artifact: Reducing DNN Properties to Enable Falsification with Adversarial Attacks","authors":"David Shriver, Sebastian G. Elbaum, Matthew B. Dwyer","doi":"10.1109/ICSE-Companion52605.2021.00068","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00068","url":null,"abstract":"We present an artifact to accompany Reducing DNN Properties to Enable Falsification with Adversarial Attacks which includes the DNNF tool, data and scripts to facilitate the replication of its study. The artifact is both reusable and available. DNNF is available on Github, and we provide an artifact to reproduce our study as a VirtualBox virtual machine image. Full replication of the study requires 64GB of memory and 8 CPU cores. Users should know how to use VirtualBox, as well as have basic knowledge of the bash shell.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132783126","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}
Pub Date : 2021-04-16DOI: 10.1109/ICSE-Companion52605.2021.00126
R. Heumüller
Modern Code Review (MCR) is a standard in all kinds of organizations that develop software.MCR pays for itself through perceived and proven benefits in quality assurance and knowledge transfer. However, the time invest in MCR is generally substantial. The goal of this thesis is to boost the efficiency of MCR by developing AI techniques that can partially replace or assist human reviewers. The envisioned techniques distinguish from existing MCR-related AI models in that we interpret these challenges as graph-learning problems. This should allow us to use state-of-science algorithms from that domain to learn coding and reviewing standards directly from existing projects. The required training data will be mined from online repositories and the experiments will be designed to use standard, quantitative evaluation metrics. This research proposal defines the motivation, research-questions, and solution components for the thesis, and gives an overview of the relevant related work.
{"title":"Learning to Boost the Efficiency of Modern Code Review","authors":"R. Heumüller","doi":"10.1109/ICSE-Companion52605.2021.00126","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00126","url":null,"abstract":"Modern Code Review (MCR) is a standard in all kinds of organizations that develop software.MCR pays for itself through perceived and proven benefits in quality assurance and knowledge transfer. However, the time invest in MCR is generally substantial. The goal of this thesis is to boost the efficiency of MCR by developing AI techniques that can partially replace or assist human reviewers. The envisioned techniques distinguish from existing MCR-related AI models in that we interpret these challenges as graph-learning problems. This should allow us to use state-of-science algorithms from that domain to learn coding and reviewing standards directly from existing projects. The required training data will be mined from online repositories and the experiments will be designed to use standard, quantitative evaluation metrics. This research proposal defines the motivation, research-questions, and solution components for the thesis, and gives an overview of the relevant related work.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131732099","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}
Pub Date : 2021-04-16DOI: 10.1109/ICSE-Companion52605.2021.00134
Manish Motwani
Automatic program repair (APR) has recently gained attention because it proposes to fix software defects with no human intervention. To automatically fix defects, most APR tools use the developer-written tests to (a) localize the defect, and (b) generate and validate the automatically produced candidate patches based on the constraints imposed by the tests. While APR tools can produce patches that appear to fix the defect for 11–19% of the defects in real-world software, most of the patches produced are not correct or acceptable to developers because they overfit to the tests used during the repair process. This problem is known as the patch overfitting problem. To address this problem, I propose to equip APR tools with additional constraints derived from natural-language software artifacts such as bug reports and requirements specifications that describe the bug and intended software behavior but are not typically used by the APR tools. I hypothesize that patches produced by APR tools while using such additional constraints would be of higher quality. To test this hypothesis, I propose an automated and objective approach to evaluate the quality of patches, and propose two novel methods to improve the fault localization and developer-written test suites using natural-language software artifacts. Finally, I propose to use my patch evaluation methodology to analyze the effect of the improved fault localization and test suites on the quality of patches produced by APR tools for real-world defects.
{"title":"High-Quality Automated Program Repair","authors":"Manish Motwani","doi":"10.1109/ICSE-Companion52605.2021.00134","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00134","url":null,"abstract":"Automatic program repair (APR) has recently gained attention because it proposes to fix software defects with no human intervention. To automatically fix defects, most APR tools use the developer-written tests to (a) localize the defect, and (b) generate and validate the automatically produced candidate patches based on the constraints imposed by the tests. While APR tools can produce patches that appear to fix the defect for 11–19% of the defects in real-world software, most of the patches produced are not correct or acceptable to developers because they overfit to the tests used during the repair process. This problem is known as the patch overfitting problem. To address this problem, I propose to equip APR tools with additional constraints derived from natural-language software artifacts such as bug reports and requirements specifications that describe the bug and intended software behavior but are not typically used by the APR tools. I hypothesize that patches produced by APR tools while using such additional constraints would be of higher quality. To test this hypothesis, I propose an automated and objective approach to evaluate the quality of patches, and propose two novel methods to improve the fault localization and developer-written test suites using natural-language software artifacts. Finally, I propose to use my patch evaluation methodology to analyze the effect of the improved fault localization and test suites on the quality of patches produced by APR tools for real-world defects.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126022945","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}
Pub Date : 2021-04-13DOI: 10.1109/ICSE-Companion52605.2021.00125
Washington Almeida
The process of contracting software factorieswithin the scope of the Federal Public Administration (APF, inPortuguese) in Brazil has undergone some changes due tolegislative alterations and a model has been proposed to improvethe rendering of services regarding delivering results. Softwarefactory contracts based on predictive processes and on themetrics of Function Points (FP) are the target of criticisms andhave problems in achieving results, as verified in previousresearch. The initial objective of the study now proposed will beto define a process model for formulating metrics that can beused in agile contracts as opposed to those of standardized FPwhich have already proved to be quite problematic and difficultfor the contractor to manage. Thus, in line with the theme ofagile contracts for software development companies, the studyproposed will seek to better understand the problem of softwaredevelopment and its maintenance by public bodies by means ofcontracts using agile methodologies and appropriate metrics forremunerating these services.
{"title":"A Model using Agile Methodologies for Defining Metrics to be used by the Public Sector in Brazil to set Remuneration for Outsourced Software Development","authors":"Washington Almeida","doi":"10.1109/ICSE-Companion52605.2021.00125","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00125","url":null,"abstract":"The process of contracting software factorieswithin the scope of the Federal Public Administration (APF, inPortuguese) in Brazil has undergone some changes due tolegislative alterations and a model has been proposed to improvethe rendering of services regarding delivering results. Softwarefactory contracts based on predictive processes and on themetrics of Function Points (FP) are the target of criticisms andhave problems in achieving results, as verified in previousresearch. The initial objective of the study now proposed will beto define a process model for formulating metrics that can beused in agile contracts as opposed to those of standardized FPwhich have already proved to be quite problematic and difficultfor the contractor to manage. Thus, in line with the theme ofagile contracts for software development companies, the studyproposed will seek to better understand the problem of softwaredevelopment and its maintenance by public bodies by means ofcontracts using agile methodologies and appropriate metrics forremunerating these services.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134176937","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}
Pub Date : 2021-04-13DOI: 10.1109/ICSE-Companion52605.2021.00123
Viet-Man Le
In large-scale feature models, feature modeling and configuration processes are highly expected to be done by a group of stakeholders. In this context, recommendation techniques can increase the efficiency of feature-model design and find optimal configurations for groups of stakeholders. Existing studies show plenty of issues concerning feature model navigation support, group members' satisfaction, and conflict resolution. This study proposes group recommendation techniques for feature modeling and configuration on the basis of addressing the mentioned issues.
{"title":"Group Recommendation Techniques for Feature Modeling and Configuration","authors":"Viet-Man Le","doi":"10.1109/ICSE-Companion52605.2021.00123","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00123","url":null,"abstract":"In large-scale feature models, feature modeling and configuration processes are highly expected to be done by a group of stakeholders. In this context, recommendation techniques can increase the efficiency of feature-model design and find optimal configurations for groups of stakeholders. Existing studies show plenty of issues concerning feature model navigation support, group members' satisfaction, and conflict resolution. This study proposes group recommendation techniques for feature modeling and configuration on the basis of addressing the mentioned issues.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126968200","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}
Pub Date : 2021-04-12DOI: 10.1109/ICSE-Companion52605.2021.00121
Raul Medeiros
The number of studies focusing on onboarding in software organizations has increased significantly during the last years. However, current literature overlooks onboarding in Software Product Lines (SPLs). SPLs have been proven effective in managing the increasing variability of industry software and enabling systematic reuse of a product family. Despite these benefits, SPLs are complex and exhibit particular characteristics that distinguish them from traditional software. Due to these peculiarities, SPLs require a tailor-made onboarding process. Assistance tools might help. In this dissertation, we propose assistance tools (i.e., tools built on top of the software project that helplearnersunderstand and developknowledge) as a means for helping newcomers during onboarding in SPLs.
{"title":"Unburdening Onboarding in Software Product Lines","authors":"Raul Medeiros","doi":"10.1109/ICSE-Companion52605.2021.00121","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00121","url":null,"abstract":"The number of studies focusing on onboarding in software organizations has increased significantly during the last years. However, current literature overlooks onboarding in Software Product Lines (SPLs). SPLs have been proven effective in managing the increasing variability of industry software and enabling systematic reuse of a product family. Despite these benefits, SPLs are complex and exhibit particular characteristics that distinguish them from traditional software. Due to these peculiarities, SPLs require a tailor-made onboarding process. Assistance tools might help. In this dissertation, we propose assistance tools (i.e., tools built on top of the software project that helplearnersunderstand and developknowledge) as a means for helping newcomers during onboarding in SPLs.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133808017","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}
Pub Date : 2021-04-12DOI: 10.1109/ICSE-Companion52605.2021.00122
Kamil Rosiak
Cloning is a general approach to create new functionality within variants as well as new system variants. It is a fast, flexible, intuitive, and economical approach to evolve systems in the short run. However, in the long run, the maintenance effort increases. A common solution to this problem is the extraction of a product line from a set of cloned variants. This process requires a detailed analysis of variants to extract variability information. However, clones within a variant are usually not considered in the process, but are also a cause for unsustainable software. This thesis proposes an extractive multi product-line engineering approach to re-establish the sustainable development of software variants. We propose an approach to re-engineer intra-system and inter-system clones into reusable, configurable components stored in an integrated platform and synthesize a matching multi-layer feature model.
{"title":"Extractive Multi Product-Line Engineering","authors":"Kamil Rosiak","doi":"10.1109/ICSE-Companion52605.2021.00122","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00122","url":null,"abstract":"Cloning is a general approach to create new functionality within variants as well as new system variants. It is a fast, flexible, intuitive, and economical approach to evolve systems in the short run. However, in the long run, the maintenance effort increases. A common solution to this problem is the extraction of a product line from a set of cloned variants. This process requires a detailed analysis of variants to extract variability information. However, clones within a variant are usually not considered in the process, but are also a cause for unsustainable software. This thesis proposes an extractive multi product-line engineering approach to re-establish the sustainable development of software variants. We propose an approach to re-engineer intra-system and inter-system clones into reusable, configurable components stored in an integrated platform and synthesize a matching multi-layer feature model.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132406792","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}
Pub Date : 2021-03-31DOI: 10.1109/ICSE-Companion52605.2021.00055
Yufeng Chen
Node.js is one of the most popular frameworks for building web applications. As software systems mature, the cost of running their entire regression test suite can become significant. Selective Regression Testing (SRT) is a technique that executes only a subset of tests the regression test suite can detect software failures more efficiently. Previous SRT studies mainly focused on standard desktop applications. Node.js applications are considered hard to perform test reduction because of Node's asynchronous, event-driven programming model and because JavaScript is a dynamic programming language. In this paper, we present NodeSRT, a Selective Regression Testing framework for Node.js applications. By performing static and dynamic analysis, NodeSRT identifies the relationship between changed methods and tests, then reduces the regression test suite to only tests that are affected by the change to improve the execution time of the regression test suite. To evaluate our selection technique, we applied NodeSRT to two open-source projects: Uppy and Simorgh, then compared our approach with the retest-all strategy and current industry-standard SRT technique: Jest OnlyChange. The results demonstrate that NodeSRT correctly selects affected tests based on changes and is 250% faster, 450% more precise than the Jest OnlyChange.
{"title":"NodeSRT: A Selective Regression Testing Tool for Node.js Application","authors":"Yufeng Chen","doi":"10.1109/ICSE-Companion52605.2021.00055","DOIUrl":"https://doi.org/10.1109/ICSE-Companion52605.2021.00055","url":null,"abstract":"Node.js is one of the most popular frameworks for building web applications. As software systems mature, the cost of running their entire regression test suite can become significant. Selective Regression Testing (SRT) is a technique that executes only a subset of tests the regression test suite can detect software failures more efficiently. Previous SRT studies mainly focused on standard desktop applications. Node.js applications are considered hard to perform test reduction because of Node's asynchronous, event-driven programming model and because JavaScript is a dynamic programming language. In this paper, we present NodeSRT, a Selective Regression Testing framework for Node.js applications. By performing static and dynamic analysis, NodeSRT identifies the relationship between changed methods and tests, then reduces the regression test suite to only tests that are affected by the change to improve the execution time of the regression test suite. To evaluate our selection technique, we applied NodeSRT to two open-source projects: Uppy and Simorgh, then compared our approach with the retest-all strategy and current industry-standard SRT technique: Jest OnlyChange. The results demonstrate that NodeSRT correctly selects affected tests based on changes and is 250% faster, 450% more precise than the Jest OnlyChange.","PeriodicalId":136929,"journal":{"name":"2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126406046","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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