Software peer review (aka. code review) is regarded as one of the most important approaches to keep software quality and productivity. Due to the distributed collaborations and communication nature of Open Source Software (OSS), OSS review differs from traditional industry review. Unlike other related works, this study investigated OSS peer review pro- cesses from social perspective by using social network anal- ysis (SNA). We analyzed the review history from three typi- cal OSS projects. The results provide hints on relationships among the OSS reviewers which can help to understand how developers work and communicate with each other.
{"title":"Social network analysis in open source software peer review","authors":"Xin Yang","doi":"10.1145/2635868.2661682","DOIUrl":"https://doi.org/10.1145/2635868.2661682","url":null,"abstract":"Software peer review (aka. code review) is regarded as one of the most important approaches to keep software quality and productivity. Due to the distributed collaborations and communication nature of Open Source Software (OSS), OSS review differs from traditional industry review. Unlike other related works, this study investigated OSS peer review pro- cesses from social perspective by using social network anal- ysis (SNA). We analyzed the review history from three typi- cal OSS projects. The results provide hints on relationships among the OSS reviewers which can help to understand how developers work and communicate with each other.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121438508","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}
D. Binkley, N. Gold, M. Harman, Syed S. Islam, J. Krinke, S. Yoo
Current slicing techniques cannot handle systems written in multiple programming languages. Observation-Based Slicing (ORBS) is a language-independent slicing technique capable of slicing multi-language systems, including systems which contain (third party) binary components. A potential slice obtained through repeated statement deletion is validated by observing the behaviour of the program: if the slice and original program behave the same under the slicing criterion, the deletion is accepted. The resulting slice is similar to a dynamic slice. We evaluate five variants of ORBS on ten programs of different sizes and languages showing that it is less expensive than similar existing techniques. We also evaluate it on bash and four other systems to demonstrate feasible large-scale operation in which a parallelised ORBS needs up to 82% less time when using four threads. The results show that an ORBS slicer is simple to construct, effective at slicing, and able to handle systems written in multiple languages without specialist analysis tools.
{"title":"ORBS: language-independent program slicing","authors":"D. Binkley, N. Gold, M. Harman, Syed S. Islam, J. Krinke, S. Yoo","doi":"10.1145/2635868.2635893","DOIUrl":"https://doi.org/10.1145/2635868.2635893","url":null,"abstract":"Current slicing techniques cannot handle systems written in multiple programming languages. Observation-Based Slicing (ORBS) is a language-independent slicing technique capable of slicing multi-language systems, including systems which contain (third party) binary components. A potential slice obtained through repeated statement deletion is validated by observing the behaviour of the program: if the slice and original program behave the same under the slicing criterion, the deletion is accepted. The resulting slice is similar to a dynamic slice. We evaluate five variants of ORBS on ten programs of different sizes and languages showing that it is less expensive than similar existing techniques. We also evaluate it on bash and four other systems to demonstrate feasible large-scale operation in which a parallelised ORBS needs up to 82% less time when using four threads. The results show that an ORBS slicer is simple to construct, effective at slicing, and able to handle systems written in multiple languages without specialist analysis tools.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127642495","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}
Senthil Mani, K. Sankaranarayanan, Vibha Sinha, Premkumar T. Devanbu
There is an increasing trend to outsource maintenance of large applications and application portfolios of a business to third parties, specialising in application maintenance, who are incented to deliver the best possible maintenance at the lowest cost. To do so, they need to identify repeat problem areas, which cause more maintenance grief, and seek a unified remedy to avoid the costs spent on fixing these individually. These repeat areas, in a sense, represent major, evolving areas of need, or requirements, for the customer. The information about the repeating problem is typically embedded in the unstructured text of multiple tickets, waiting to be found and addressed. Currently, repeat problems are found by manual analysis; effective solutions depend on the collective experience of the team solving them. In this paper, we propose an approach to automatically analyze problem tickets to discover groups of problems being reported in them and provide meaningful, descriptive labels to help interpret these groups. Our approach incorporates a cleansing phase to handle the high level of noise observed in problem tickets and a method to incorporate multiple text clustering techniques and merge their results in a meaningful manner. We provide detailed experiments to quantitatively and qualitatively evaluate our approach
{"title":"Panning requirement nuggets in stream of software maintenance tickets","authors":"Senthil Mani, K. Sankaranarayanan, Vibha Sinha, Premkumar T. Devanbu","doi":"10.1145/2635868.2635897","DOIUrl":"https://doi.org/10.1145/2635868.2635897","url":null,"abstract":"There is an increasing trend to outsource maintenance of large applications and application portfolios of a business to third parties, specialising in application maintenance, who are incented to deliver the best possible maintenance at the lowest cost. To do so, they need to identify repeat problem areas, which cause more maintenance grief, and seek a unified remedy to avoid the costs spent on fixing these individually. These repeat areas, in a sense, represent major, evolving areas of need, or requirements, for the customer. The information about the repeating problem is typically embedded in the unstructured text of multiple tickets, waiting to be found and addressed. Currently, repeat problems are found by manual analysis; effective solutions depend on the collective experience of the team solving them. In this paper, we propose an approach to automatically analyze problem tickets to discover groups of problems being reported in them and provide meaningful, descriptive labels to help interpret these groups. Our approach incorporates a cleansing phase to handle the high level of noise observed in problem tickets and a method to incorporate multiple text clustering techniques and merge their results in a meaningful manner. We provide detailed experiments to quantitatively and qualitatively evaluate our approach","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132986597","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}
Ensuring safety-critical software safety requires strict verification of the conformance between safety requirements and programs. Formal verification techniques, such as model checking and theorem proving, can be used to partially realize this objective. DO-178C, a standard for airborne systems, allows formal verification techniques to replace certain forms of testing. My research is concerned with applying model checking to verify the conformance between safety requirements and programs. First, a formal language for specifying software safety requirements which are relevant to event sequences is introduced. Second, the traceability information models between formalized safety requirements and programs are built. Third, the checking of a program against a safety requirement is decomposed into smaller model checking problems by utilizing traceability information model between them.
{"title":"Traceability and model checking to support safety requirement verification","authors":"Shuanglong Kan","doi":"10.1145/2635868.2666606","DOIUrl":"https://doi.org/10.1145/2635868.2666606","url":null,"abstract":"Ensuring safety-critical software safety requires strict verification of the conformance between safety requirements and programs. Formal verification techniques, such as model checking and theorem proving, can be used to partially realize this objective. DO-178C, a standard for airborne systems, allows formal verification techniques to replace certain forms of testing. My research is concerned with applying model checking to verify the conformance between safety requirements and programs. First, a formal language for specifying software safety requirements which are relevant to event sequences is introduced. Second, the traceability information models between formalized safety requirements and programs are built. Third, the checking of a program against a safety requirement is decomposed into smaller model checking problems by utilizing traceability information model between them.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131688810","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}
Ferdian Thung, Tien-Duy B. Le, Pavneet Singh Kochhar, D. Lo
To manage bugs that appear in a software, developers often make use of a bug tracking system such as Bugzilla. Users can report bugs that they encounter in such a system. Whenever a user reports a new bug report, developers need to read the summary and description of the bug report and manually locate the buggy files based on this information. This manual process is often time consuming and tedious. Thus, a number of past studies have proposed bug localization techniques to automatically recover potentially buggy files from bug reports. Unfortunately, none of these techniques are integrated to bug tracking systems and thus it hinders their adoption by practitioners. To help disseminate research in bug localization to practitioners, we develop a tool named BugLocalizer, which is implemented as a Bugzilla extension and builds upon a recently proposed bug localization technique. Our tool extracts texts from summary and description fields of a bug report and source code files. It then computes similarities of the bug report with source code files to find the buggy files. Developers can use our tool online from a Bugzilla web interface by providing a link to a git source code repository and specifying the version of the repository to be analyzed. We have released our tool publicly in GitHub, which is available at: https://github.com/smagsmu/buglocalizer. We have also provided a demo video, which can be accessed at: http://youtu.be/iWHaLNCUjBY.
{"title":"BugLocalizer: integrated tool support for bug localization","authors":"Ferdian Thung, Tien-Duy B. Le, Pavneet Singh Kochhar, D. Lo","doi":"10.1145/2635868.2661678","DOIUrl":"https://doi.org/10.1145/2635868.2661678","url":null,"abstract":"To manage bugs that appear in a software, developers often make use of a bug tracking system such as Bugzilla. Users can report bugs that they encounter in such a system. Whenever a user reports a new bug report, developers need to read the summary and description of the bug report and manually locate the buggy files based on this information. This manual process is often time consuming and tedious. Thus, a number of past studies have proposed bug localization techniques to automatically recover potentially buggy files from bug reports. Unfortunately, none of these techniques are integrated to bug tracking systems and thus it hinders their adoption by practitioners. To help disseminate research in bug localization to practitioners, we develop a tool named BugLocalizer, which is implemented as a Bugzilla extension and builds upon a recently proposed bug localization technique. Our tool extracts texts from summary and description fields of a bug report and source code files. It then computes similarities of the bug report with source code files to find the buggy files. Developers can use our tool online from a Bugzilla web interface by providing a link to a git source code repository and specifying the version of the repository to be analyzed. We have released our tool publicly in GitHub, which is available at: https://github.com/smagsmu/buglocalizer. We have also provided a demo video, which can be accessed at: http://youtu.be/iWHaLNCUjBY.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"26 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121042431","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 past years, widely used platforms such as the Java Class Library have been under constant attack through vulnerabilities that involve a combination of two taint-analysis problems: an integrity problem allowing attackers to trigger sensitive operations within the platform, and a confidentiality problem allowing the attacker to retrieve sensitive information or pointers from the results of those operations. While existing static taint analyses are good at solving either of those problems, we show that they scale prohibitively badly when being applied to situations that require the exploitation of both an integrity and confidentiality problem in combination. The main problem is the huge attack surface of libraries such as the Java Class Library, which exposes thousands of methods potentially controllable by an attacker. In this work we thus present FlowTwist, a novel taint-analysis approach that works inside-out, i.e., tracks data flows from potentially vulnerable calls to the outer level of the API which the attacker might control. This inside-out analysis requires a careful, context-sensitive coordination of both a backward and a forward taint analysis. In this work, we expose a design of the analysis approach based on the IFDS algorithm, and explain several extensions to IFDS that enable not only this coordination but also a helpful reporting of error situations to security analysts. Experiments with the Java Class Library show that, while a simple forward taint-analysis approach does not scale even with much machine power, FlowTwist's algorithm is able to fully analyze the library within 10 minutes.
在过去的几年中，广泛使用的平台(如Java Class Library)一直受到漏洞的持续攻击，这些漏洞涉及两个污染分析问题的组合:完整性问题允许攻击者触发平台内的敏感操作，机密性问题允许攻击者从这些操作的结果中检索敏感信息或指针。虽然现有的静态污染分析很好地解决了这些问题中的任何一个，但我们表明，当应用于需要同时利用完整性和机密性问题的情况时，它们的可伸缩性非常糟糕。主要问题是库(如Java Class Library)的巨大攻击面，它暴露了攻击者可能控制的数千种方法。在这项工作中，我们因此提出了FlowTwist，这是一种新颖的由内而外的污染分析方法，即跟踪从潜在易受攻击的调用到攻击者可能控制的API外部级别的数据流。这种由内而外的分析需要仔细地、上下文敏感地协调向后和向前的污染分析。在这项工作中，我们揭示了基于IFDS算法的分析方法的设计，并解释了IFDS的几个扩展，这些扩展不仅可以实现这种协调，还可以帮助向安全分析师报告错误情况。Java类库的实验表明，虽然简单的前向污染分析方法即使在机器功率很大的情况下也无法扩展，但FlowTwist的算法能够在10分钟内完全分析该库。
{"title":"FlowTwist: efficient context-sensitive inside-out taint analysis for large codebases","authors":"Johannes Lerch, Ben Hermann, E. Bodden, M. Mezini","doi":"10.1145/2635868.2635878","DOIUrl":"https://doi.org/10.1145/2635868.2635878","url":null,"abstract":"Over the past years, widely used platforms such as the Java Class Library have been under constant attack through vulnerabilities that involve a combination of two taint-analysis problems: an integrity problem allowing attackers to trigger sensitive operations within the platform, and a confidentiality problem allowing the attacker to retrieve sensitive information or pointers from the results of those operations. While existing static taint analyses are good at solving either of those problems, we show that they scale prohibitively badly when being applied to situations that require the exploitation of both an integrity and confidentiality problem in combination. The main problem is the huge attack surface of libraries such as the Java Class Library, which exposes thousands of methods potentially controllable by an attacker. In this work we thus present FlowTwist, a novel taint-analysis approach that works inside-out, i.e., tracks data flows from potentially vulnerable calls to the outer level of the API which the attacker might control. This inside-out analysis requires a careful, context-sensitive coordination of both a backward and a forward taint analysis. In this work, we expose a design of the analysis approach based on the IFDS algorithm, and explain several extensions to IFDS that enable not only this coordination but also a helpful reporting of error situations to security analysts. Experiments with the Java Class Library show that, while a simple forward taint-analysis approach does not scale even with much machine power, FlowTwist's algorithm is able to fully analyze the library within 10 minutes.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121321054","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}
When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files of a project with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and potentially could lead to a substantial increase in productivity. This paper introduces an adaptive ranking approach that leverages domain knowledge through functional decompositions of source code files into methods, API descriptions of library components used in the code, the bug-fixing history, and the code change history. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features encoding domain knowledge, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. We evaluated our system on six large scale open source Java projects, using the before-fix version of the project for every bug report. The experimental results show that the newly introduced learning-to-rank approach significantly outperforms two recent state-of-the-art methods in recommending relevant files for bug reports. In particular, our method makes correct recommendations within the top 10 ranked source files for over 70% of the bug reports in the Eclipse Platform and Tomcat projects.
{"title":"Learning to rank relevant files for bug reports using domain knowledge","authors":"Xin Ye, Razvan C. Bunescu, Chang Liu","doi":"10.1145/2635868.2635874","DOIUrl":"https://doi.org/10.1145/2635868.2635874","url":null,"abstract":"When a new bug report is received, developers usually need to reproduce the bug and perform code reviews to find the cause, a process that can be tedious and time consuming. A tool for ranking all the source files of a project with respect to how likely they are to contain the cause of the bug would enable developers to narrow down their search and potentially could lead to a substantial increase in productivity. This paper introduces an adaptive ranking approach that leverages domain knowledge through functional decompositions of source code files into methods, API descriptions of library components used in the code, the bug-fixing history, and the code change history. Given a bug report, the ranking score of each source file is computed as a weighted combination of an array of features encoding domain knowledge, where the weights are trained automatically on previously solved bug reports using a learning-to-rank technique. We evaluated our system on six large scale open source Java projects, using the before-fix version of the project for every bug report. The experimental results show that the newly introduced learning-to-rank approach significantly outperforms two recent state-of-the-art methods in recommending relevant files for bug reports. In particular, our method makes correct recommendations within the top 10 ranked source files for over 70% of the bug reports in the Eclipse Platform and Tomcat projects.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129012322","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}
The idea of software ecosystems encourages organizations to open software projects for external businesses, governing the cross-organizational development by architectural and other measures. Even within a single organization, this paradigm can be of high value for large-scale decentralized software projects that involve various internal, yet self-contained organizational units. However, this intra-organizational decentralization causes architecture challenges that must be understood to reason about suitable architectural measures. We present an in-depth case study on collaboration and architecture challenges in two of these large-scale software projects at Siemens. We performed a total of 46 hours of semi-structured interviews with 17 leading software architects from all involved organizational units. Our major findings are: (1) three collaboration models on a continuum that ranges from high to low coupling, (2) a classification of architecture challenges, together with (3) a qualitative and quantitative exposure of the identified recurring issues along each collaboration model. Our study results provide valuable insights for both industry and academia: Practitioners that find themselves in one of the collaboration models can use empirical evidence on challenges to make informed decisions about counteractive measures. Researchers can focus their attention on challenges faced by practitioners to make software engineering more effective.
{"title":"Architecture challenges for internal software ecosystems: a large-scale industry case study","authors":"K. Schultis, Christoph Elsner, D. Lohmann","doi":"10.1145/2635868.2635876","DOIUrl":"https://doi.org/10.1145/2635868.2635876","url":null,"abstract":"The idea of software ecosystems encourages organizations to open software projects for external businesses, governing the cross-organizational development by architectural and other measures. Even within a single organization, this paradigm can be of high value for large-scale decentralized software projects that involve various internal, yet self-contained organizational units. However, this intra-organizational decentralization causes architecture challenges that must be understood to reason about suitable architectural measures. We present an in-depth case study on collaboration and architecture challenges in two of these large-scale software projects at Siemens. We performed a total of 46 hours of semi-structured interviews with 17 leading software architects from all involved organizational units. Our major findings are: (1) three collaboration models on a continuum that ranges from high to low coupling, (2) a classification of architecture challenges, together with (3) a qualitative and quantitative exposure of the identified recurring issues along each collaboration model. Our study results provide valuable insights for both industry and academia: Practitioners that find themselves in one of the collaboration models can use empirical evidence on challenges to make informed decisions about counteractive measures. Researchers can focus their attention on challenges faced by practitioners to make software engineering more effective.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116337045","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}
Action flicks and the analysis of software data in industry have more in common than you think. Both action heroes and development teams are on tight deadlines to save the day. Getting wrong information can lead to disastrous outcomes. In this talk, I will share experiences from my six years of research in the Empirical Software Engineering Group working with engineers towards sound data-driven decision about software.
{"title":"Data hard with a vengeance (invited talk)","authors":"Thomas Zimmermann","doi":"10.1145/2635868.2684431","DOIUrl":"https://doi.org/10.1145/2635868.2684431","url":null,"abstract":"Action flicks and the analysis of software data in industry have more in common than you think. Both action heroes and development teams are on tight deadlines to save the day. Getting wrong information can lead to disastrous outcomes. In this talk, I will share experiences from my six years of research in the Empirical Software Engineering Group working with engineers towards sound data-driven decision about software.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116368725","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}
Inexperienced software developers - for example, undergraduates entering the workforce - exhibit a lack of testing skills. They have trouble understanding and applying basic testing techniques. These inexperienced developers are hired by software companies, where this lack of testing skills has already been recognized. Companies allocate valuable resources and invest time and money in different onboarding strategies to introduce new hires to the organization’s testing practices. However, if the lack of testing skills is not addressed properly, the new hire is left to her own devices. This hinders her in becoming a high-quality engineer for the software company. This thesis proposes to improve the onboarding strategies with traits of social transparency in order to specifically address testing issues of inexperienced new hires. Social transparency has been shown to influence the testing behavior of development teams on a social coding site. An environment that is open for discussion helps newcomers to understand and adapt a team’s testing culture. Tailoring the onboarding process to better address testing skills of new hires makes it more effective and more efficient. This reduces the danger of carrying new hire’s testing deficits into commercial software development.
{"title":"Improving the software testing skills of novices during onboarding through social transparency","authors":"Raphael Pham","doi":"10.1145/2635868.2666604","DOIUrl":"https://doi.org/10.1145/2635868.2666604","url":null,"abstract":"Inexperienced software developers - for example, undergraduates entering the workforce - exhibit a lack of testing skills. They have trouble understanding and applying basic testing techniques. These inexperienced developers are hired by software companies, where this lack of testing skills has already been recognized. Companies allocate valuable resources and invest time and money in different onboarding strategies to introduce new hires to the organization’s testing practices. However, if the lack of testing skills is not addressed properly, the new hire is left to her own devices. This hinders her in becoming a high-quality engineer for the software company. This thesis proposes to improve the onboarding strategies with traits of social transparency in order to specifically address testing issues of inexperienced new hires. Social transparency has been shown to influence the testing behavior of development teams on a social coding site. An environment that is open for discussion helps newcomers to understand and adapt a team’s testing culture. Tailoring the onboarding process to better address testing skills of new hires makes it more effective and more efficient. This reduces the danger of carrying new hire’s testing deficits into commercial software development.","PeriodicalId":250543,"journal":{"name":"Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering","volume":"12 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114109225","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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