Pub Date : 2019-09-01DOI: 10.1109/ICSME.2019.00058
Hyrum K. Wright
Google maintains a large multi-language codebase containing hundreds of millions of lines code across many different projects. Over the past several years, our team has developed processes which enable us to update that codebase efficiently at scale. In this talk, I discuss some of the lessons which we have learned and existing open problems in the space.
{"title":"Lessons Learned from Large-Scale Refactoring","authors":"Hyrum K. Wright","doi":"10.1109/ICSME.2019.00058","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00058","url":null,"abstract":"Google maintains a large multi-language codebase containing hundreds of millions of lines code across many different projects. Over the past several years, our team has developed processes which enable us to update that codebase efficiently at scale. In this talk, I discuss some of the lessons which we have learned and existing open problems in the space.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125861730","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00075
G. Nery, D. A. D. Costa, U. Kulesza
Continuous Integration (CI) is the practice of automating and improving the frequency of code integration. CI has been widely adopted by software development teams and has brought the attention of researchers to study its benefits. Existing research shows that CI can improve software quality by identifying the errors earlier in the software development life-cycle. One question that remains open, however, is whether CI increases the adoption of testing practices in software projects. The goal of our work is to investigate the evolution of software tests and its relationship with the adoption of Continuous Integration. We set out to compare 82 projects that adopted CI (CI projects) and 82 projects that have never adopted CI (NOCI projects). In total, we studied 3,936 versions of our studied projects to investigate trends on the test code ratio and coverage. We observe that 40.2% of the CI projects have a rising test-code ratio trend while only 17% of the NOCI projects have a rising trend. Additionally, we find evidences that CI projects have improved the overall test coverage while NOCI projects do not experience the same growth. Finally, we build a mixed-effects model to study software development factors than can possibly explain the test ratio. Our models reveal that test ratio is largely explained by the project inherent context rather than code or process factors. In overall, our work demonstrates that Continuous Integration can be empirically associated with a healthier test code evolution.
{"title":"An Empirical Study of the Relationship between Continuous Integration and Test Code Evolution","authors":"G. Nery, D. A. D. Costa, U. Kulesza","doi":"10.1109/ICSME.2019.00075","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00075","url":null,"abstract":"Continuous Integration (CI) is the practice of automating and improving the frequency of code integration. CI has been widely adopted by software development teams and has brought the attention of researchers to study its benefits. Existing research shows that CI can improve software quality by identifying the errors earlier in the software development life-cycle. One question that remains open, however, is whether CI increases the adoption of testing practices in software projects. The goal of our work is to investigate the evolution of software tests and its relationship with the adoption of Continuous Integration. We set out to compare 82 projects that adopted CI (CI projects) and 82 projects that have never adopted CI (NOCI projects). In total, we studied 3,936 versions of our studied projects to investigate trends on the test code ratio and coverage. We observe that 40.2% of the CI projects have a rising test-code ratio trend while only 17% of the NOCI projects have a rising trend. Additionally, we find evidences that CI projects have improved the overall test coverage while NOCI projects do not experience the same growth. Finally, we build a mixed-effects model to study software development factors than can possibly explain the test ratio. Our models reveal that test ratio is largely explained by the project inherent context rather than code or process factors. In overall, our work demonstrates that Continuous Integration can be empirically associated with a healthier test code evolution.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122932856","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00085
John Johnson, Sergio Lubo, Nishitha Yedla, Jairo Aponte, Bonita Sharif
Software developers spend a significant amount of time reading source code. If code is not written with readability in mind, it impacts the time required to maintain it. In order to alleviate the time taken to read and understand code, it is important to consider how readable the code is. The general consensus is that source code should be written to minimize the time it takes for others to read and understand it. In this paper, we conduct a controlled experiment to assess two code readability rules: nesting and looping. We test 32 Java methods in four categories: ones that follow/do not follow the readability rule and that are correct/incorrect. The study was conducted online with 275 participants. The results indicate that minimizing nesting decreases the time a developer spends reading and understanding source code, increases confidence about the developer's understanding of the code, and also suggests that it improves their ability to find bugs. The results also show that avoiding the do-while statement had no significant impact on level of understanding, time spent reading and understanding, confidence in understanding, or ease of finding bugs. It was also found that the better knowledge of English a participant had, the more their readability and comprehension confidence ratings were affected by the minimize nesting rule. We discuss the implications of these findings for code readability and comprehension.
{"title":"An Empirical Study Assessing Source Code Readability in Comprehension","authors":"John Johnson, Sergio Lubo, Nishitha Yedla, Jairo Aponte, Bonita Sharif","doi":"10.1109/ICSME.2019.00085","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00085","url":null,"abstract":"Software developers spend a significant amount of time reading source code. If code is not written with readability in mind, it impacts the time required to maintain it. In order to alleviate the time taken to read and understand code, it is important to consider how readable the code is. The general consensus is that source code should be written to minimize the time it takes for others to read and understand it. In this paper, we conduct a controlled experiment to assess two code readability rules: nesting and looping. We test 32 Java methods in four categories: ones that follow/do not follow the readability rule and that are correct/incorrect. The study was conducted online with 275 participants. The results indicate that minimizing nesting decreases the time a developer spends reading and understanding source code, increases confidence about the developer's understanding of the code, and also suggests that it improves their ability to find bugs. The results also show that avoiding the do-while statement had no significant impact on level of understanding, time spent reading and understanding, confidence in understanding, or ease of finding bugs. It was also found that the better knowledge of English a participant had, the more their readability and comprehension confidence ratings were affected by the minimize nesting rule. We discuss the implications of these findings for code readability and comprehension.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128803576","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}
App responsiveness is the most intuitive interpretation of app performance from user's perspective. Traditional performance profilers only focus on one kind of program activities (e.g., CPU profiling), while the cause for slow responsiveness is diverse or even due to the joint effect of multiple kinds. Also, various test configurations, such as device hardware and wireless connectivity can have dramatic impact on particular program activities and indirectly affect app responsiveness. Conventional mobile testing lacks mechanisms to reveal configuration-sensitive bugs. In this paper, we propose AppSPIN, a tool to automatically diagnose app responsiveness bugs and systematically explore configuration-sensitive bugs. AppSPIN instruments the app to collect program events and UI responsiveness. The instrumented app is exercised with automated monkey testers and AppSPIN correlates excessive and lengthy program events with bad responsiveness detected at runtime. The diagnosis process also synthesizes the major resource bottleneck for the app. After one test run, AppSPIN automatically alters the test configuration to with most bottlenecked resource to further explore responsiveness bugs happened only with particular test configurations. Our preliminary experiments with 30 real-world apps show that AppSPIN can detect 123 responsiveness bugs and successfully diagnose the cause for 87% cases, within an average of 15-minute test time. Also with altered test configurations, AppSPIN uncovers a notable number of new bugs within four extra test runs.
{"title":"Systematically Testing and Diagnosing Responsiveness for Android Apps","authors":"Wenhua Zhao, Zhenkai Ding, Mingyuan Xia, Zhengwei Qi","doi":"10.1109/ICSME.2019.00077","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00077","url":null,"abstract":"App responsiveness is the most intuitive interpretation of app performance from user's perspective. Traditional performance profilers only focus on one kind of program activities (e.g., CPU profiling), while the cause for slow responsiveness is diverse or even due to the joint effect of multiple kinds. Also, various test configurations, such as device hardware and wireless connectivity can have dramatic impact on particular program activities and indirectly affect app responsiveness. Conventional mobile testing lacks mechanisms to reveal configuration-sensitive bugs. In this paper, we propose AppSPIN, a tool to automatically diagnose app responsiveness bugs and systematically explore configuration-sensitive bugs. AppSPIN instruments the app to collect program events and UI responsiveness. The instrumented app is exercised with automated monkey testers and AppSPIN correlates excessive and lengthy program events with bad responsiveness detected at runtime. The diagnosis process also synthesizes the major resource bottleneck for the app. After one test run, AppSPIN automatically alters the test configuration to with most bottlenecked resource to further explore responsiveness bugs happened only with particular test configurations. Our preliminary experiments with 30 real-world apps show that AppSPIN can detect 123 responsiveness bugs and successfully diagnose the cause for 87% cases, within an average of 15-minute test time. Also with altered test configurations, AppSPIN uncovers a notable number of new bugs within four extra test runs.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122049695","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00023
Danielle Gonzalez, Holly Hastings, Mehdi Mirakhorli
Preventing vulnerability exploits is a critical software maintenance task, and software engineers often rely on Common Vulnerability and Exposure (CVEs) reports for information about vulnerable systems and libraries. These reports include descriptions, disclosure sources, and manually-populated vulnerability characteristics such as root cause from the NIST Vulnerability Description Ontology (VDO). This information needs to be complete and accurate so stakeholders of affected products can prevent and react to exploits of the reported vulnerabilities. In this study, we demonstrate that VDO characteristics can be automatically detected from the textual descriptions included in CVE reports. We evaluated the performance of 6 classification algorithms with a dataset of 365 vulnerability descriptions, each mapped to 1 of 19 characteristics from the VDO. This work demonstrates that it is feasible to train classification techniques to accurately characterize vulnerabilities from their descriptions. All 6 classifiers evaluated produced accurate results, and the Support Vector Machine classifier was the best-performing individual classifier. Automating the vulnerability characterization process is a step towards ensuring stakeholders have the necessary data to effectively maintain their systems.
防止漏洞被利用是一项重要的软件维护任务，软件工程师经常依赖于公共漏洞和暴露(Common vulnerability and Exposure, cve)报告来获取有关易受攻击的系统和库的信息。这些报告包括描述、披露来源和手动填充的漏洞特征，例如来自NIST漏洞描述本体(VDO)的根本原因。此信息需要完整和准确，以便受影响产品的涉众可以预防并对报告的漏洞利用作出反应。在本研究中，我们证明了VDO特征可以从CVE报告中的文本描述中自动检测出来。我们使用365个漏洞描述的数据集评估了6种分类算法的性能，每个漏洞描述映射到来自VDO的19个特征中的1个。这项工作表明，训练分类技术从漏洞描述中准确地表征漏洞是可行的。所有6个分类器评估产生准确的结果，支持向量机分类器是表现最好的单个分类器。自动化漏洞表征过程是确保涉众拥有必要数据以有效维护其系统的一个步骤。
{"title":"Automated Characterization of Software Vulnerabilities","authors":"Danielle Gonzalez, Holly Hastings, Mehdi Mirakhorli","doi":"10.1109/ICSME.2019.00023","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00023","url":null,"abstract":"Preventing vulnerability exploits is a critical software maintenance task, and software engineers often rely on Common Vulnerability and Exposure (CVEs) reports for information about vulnerable systems and libraries. These reports include descriptions, disclosure sources, and manually-populated vulnerability characteristics such as root cause from the NIST Vulnerability Description Ontology (VDO). This information needs to be complete and accurate so stakeholders of affected products can prevent and react to exploits of the reported vulnerabilities. In this study, we demonstrate that VDO characteristics can be automatically detected from the textual descriptions included in CVE reports. We evaluated the performance of 6 classification algorithms with a dataset of 365 vulnerability descriptions, each mapped to 1 of 19 characteristics from the VDO. This work demonstrates that it is feasible to train classification techniques to accurately characterize vulnerabilities from their descriptions. All 6 classifiers evaluated produced accurate results, and the Support Vector Machine classifier was the best-performing individual classifier. Automating the vulnerability characterization process is a step towards ensuring stakeholders have the necessary data to effectively maintain their systems.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124374679","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00027
N. Anquetil, Anne Etien, Gaelle Andreo, Stéphane Ducasse
A group of developers at Siemens Digital Industry Division approached our team to help them restructure a large legacy system. Several problems were identified, including the presence of God classes (big classes with thousands of lines of code and hundred of methods). They had tried different approaches considering the dependencies between the classes, but none were satisfactory. Through interaction during the last three years with a lead software architect of the project, we designed a software visualization tool and an accompanying process that allows her to propose a decomposition of a God Class in a matter of one or two hours even without prior knowledge of the class (although actually implementing the decomposition in the source code could take a week of work). In this paper, we present the process that was formalized to decompose God Classes and the tool that was designed. We give details on the system itself and some of the classes that were decomposed. The presented process and visualizations have been successfully used for the last three years on a real industrial system at Siemens.
{"title":"Decomposing God Classes at Siemens","authors":"N. Anquetil, Anne Etien, Gaelle Andreo, Stéphane Ducasse","doi":"10.1109/ICSME.2019.00027","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00027","url":null,"abstract":"A group of developers at Siemens Digital Industry Division approached our team to help them restructure a large legacy system. Several problems were identified, including the presence of God classes (big classes with thousands of lines of code and hundred of methods). They had tried different approaches considering the dependencies between the classes, but none were satisfactory. Through interaction during the last three years with a lead software architect of the project, we designed a software visualization tool and an accompanying process that allows her to propose a decomposition of a God Class in a matter of one or two hours even without prior knowledge of the class (although actually implementing the decomposition in the source code could take a week of work). In this paper, we present the process that was formalized to decompose God Classes and the tool that was designed. We give details on the system itself and some of the classes that were decomposed. The presented process and visualizations have been successfully used for the last three years on a real industrial system at Siemens.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114854625","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00061
Tong Wu, Qingshan Li, Lu Wang
Self-Adaptive Systems (SASs) can dynamically adjust themselves to adapt to the changes, by planning some strategies to guide the adjustments. However, many uncertainties in runtime affect the efficiency of strategies and the ability to attain goals for SASs. So, it is necessary to validate strategies before they are executed. At present, most of the validation methods ignore the fact that less data can be observed at runtime, so it is difficult to describe the state accurately with these data. Most methods do not support uncertain state reasoning, but state transition is uncertain. This will result in the verification method not dealing well with the uncertainties. This paper proposes a strategy validation method based on Partially Observable Markov Decision Process, and makes several key contributions: (1) a uncertain state model that not only support description of states through partial information, but also describe the uncertain transitions of states, (2) a validation method to validate whether the strategies meets the requirements at runtime, (3) a strategy correction method to get effective strategies as quickly as possible.
{"title":"A Validation Method of Self-Adaptive Strategy Based on POMDP","authors":"Tong Wu, Qingshan Li, Lu Wang","doi":"10.1109/ICSME.2019.00061","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00061","url":null,"abstract":"Self-Adaptive Systems (SASs) can dynamically adjust themselves to adapt to the changes, by planning some strategies to guide the adjustments. However, many uncertainties in runtime affect the efficiency of strategies and the ability to attain goals for SASs. So, it is necessary to validate strategies before they are executed. At present, most of the validation methods ignore the fact that less data can be observed at runtime, so it is difficult to describe the state accurately with these data. Most methods do not support uncertain state reasoning, but state transition is uncertain. This will result in the verification method not dealing well with the uncertainties. This paper proposes a strategy validation method based on Partially Observable Markov Decision Process, and makes several key contributions: (1) a uncertain state model that not only support description of states through partial information, but also describe the uncertain transitions of states, (2) a validation method to validate whether the strategies meets the requirements at runtime, (3) a strategy correction method to get effective strategies as quickly as possible.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115020149","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00014
Christopher Vendome, Diana Solano, Santiago Linan, M. Linares-Vásquez
Universal design principles aim to improve accessibility by ensuring product designs consider all users, including those with certain disabilities (e.g., visual impairments). In the case of mobile apps, accessibility is mostly provided by existing features in mobile devices, like TalkBack on Android that reads information to users. However, it is not clear to what extent developers actually implement universal design principles or utilize these technologies to support accessibility of their applications. By performing a mining-based pilot study, we observed developers seldom use Accessibility APIs and there is a limited usage of assistive descriptions. Then, we focused on understanding the perspective of developers through an investigation of posts from StackOverflow. We identified the aspects of accessibility that developers implemented as well as experienced difficulty (or lack of understanding). We performed a formal open-coding of 366 discussions threads with multi-author agreement to create a taxonomy regarding the aspects discussed by developers with respect to accessibility in Android. From the qualitative analysis, we distilled lessons to guide further research and actions in aiding developers with supporting users that require assistive features.
{"title":"Can Everyone use my app? An Empirical Study on Accessibility in Android Apps","authors":"Christopher Vendome, Diana Solano, Santiago Linan, M. Linares-Vásquez","doi":"10.1109/ICSME.2019.00014","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00014","url":null,"abstract":"Universal design principles aim to improve accessibility by ensuring product designs consider all users, including those with certain disabilities (e.g., visual impairments). In the case of mobile apps, accessibility is mostly provided by existing features in mobile devices, like TalkBack on Android that reads information to users. However, it is not clear to what extent developers actually implement universal design principles or utilize these technologies to support accessibility of their applications. By performing a mining-based pilot study, we observed developers seldom use Accessibility APIs and there is a limited usage of assistive descriptions. Then, we focused on understanding the perspective of developers through an investigation of posts from StackOverflow. We identified the aspects of accessibility that developers implemented as well as experienced difficulty (or lack of understanding). We performed a formal open-coding of 366 discussions threads with multi-author agreement to create a taxonomy regarding the aspects discussed by developers with respect to accessibility in Android. From the qualitative analysis, we distilled lessons to guide further research and actions in aiding developers with supporting users that require assistive features.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125818043","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00066
Tam The Nguyen, P. Vu, T. Nguyen
Exception handling is an advanced programming technique to prevent run-time errors or crashes for modern software systems. However, inexperienced programmers might fail to write proper exception handling code in their programs. In this paper, we introduce ExAssist, a code recommendation tool for exception handling. ExAssist can predict what types of exception could occur in a given piece of code and recommend proper exception handling code for such exceptions. Preliminary evaluation of ExAssist suggests that it provides highly accurate recommendations. The tool's website is at rebrand.ly/exassist. The video demo of ExAssist could be found at rebrand.ly/exassistdemo.
{"title":"Recommending Exception Handling Code","authors":"Tam The Nguyen, P. Vu, T. Nguyen","doi":"10.1109/ICSME.2019.00066","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00066","url":null,"abstract":"Exception handling is an advanced programming technique to prevent run-time errors or crashes for modern software systems. However, inexperienced programmers might fail to write proper exception handling code in their programs. In this paper, we introduce ExAssist, a code recommendation tool for exception handling. ExAssist can predict what types of exception could occur in a given piece of code and recommend proper exception handling code for such exceptions. Preliminary evaluation of ExAssist suggests that it provides highly accurate recommendations. The tool's website is at rebrand.ly/exassist. The video demo of ExAssist could be found at rebrand.ly/exassistdemo.","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125824400","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 : 2019-09-01DOI: 10.1109/ICSME.2019.00101
Yutaro Kashiwa
Over a decade, many bug assignment methods have been proposed in order to assist developers to read bug reports submitted daily and numerously, and to assign an appropriate developer. However, they tend to concentrate their assignments on a small number of particular developers. Applying the methods to the projects which have releases would reduce the number of bugs that developers can fix by the next release date because the time that developers can devote to bug-fixing is limited. In this study, we propose the release-aware bug-fixing task assignment method to mitigate the task concentration and increase the number of bugs that developers can fix by the next release date. This method employs mathematical programming to find the best combination at project level while the traditional methods find the best pair of a bug and a developer (at individual level).
{"title":"RAPTOR: Release-Aware and Prioritized Bug-Fixing Task Assignment Optimization","authors":"Yutaro Kashiwa","doi":"10.1109/ICSME.2019.00101","DOIUrl":"https://doi.org/10.1109/ICSME.2019.00101","url":null,"abstract":"Over a decade, many bug assignment methods have been proposed in order to assist developers to read bug reports submitted daily and numerously, and to assign an appropriate developer. However, they tend to concentrate their assignments on a small number of particular developers. Applying the methods to the projects which have releases would reduce the number of bugs that developers can fix by the next release date because the time that developers can devote to bug-fixing is limited. In this study, we propose the release-aware bug-fixing task assignment method to mitigate the task concentration and increase the number of bugs that developers can fix by the next release date. This method employs mathematical programming to find the best combination at project level while the traditional methods find the best pair of a bug and a developer (at individual level).","PeriodicalId":106748,"journal":{"name":"2019 IEEE International Conference on Software Maintenance and Evolution (ICSME)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129757861","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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