Pub Date : 2012-06-02DOI: 10.1109/MSR.2012.6224267
M. Asaduzzaman, Michael C. Bullock, C. Roy, Kevin A. Schneider
Changes, a rather inevitable part of software development can cause maintenance implications if they introduce bugs into the system. By isolating and characterizing these bug introducing changes it is possible to uncover potential risky source code entities or issues that produce bugs. In this paper, we mine the bug introducing changes in the Android platform by mapping bug reports to the changes that introduced the bugs. We then use the change information to look for both potential problematic parts and dynamics in development that can cause maintenance implications. We believe that the results of our study can help better manage Android software development.
{"title":"Bug introducing changes: A case study with Android","authors":"M. Asaduzzaman, Michael C. Bullock, C. Roy, Kevin A. Schneider","doi":"10.1109/MSR.2012.6224267","DOIUrl":"https://doi.org/10.1109/MSR.2012.6224267","url":null,"abstract":"Changes, a rather inevitable part of software development can cause maintenance implications if they introduce bugs into the system. By isolating and characterizing these bug introducing changes it is possible to uncover potential risky source code entities or issues that produce bugs. In this paper, we mine the bug introducing changes in the Android platform by mapping bug reports to the changes that introduced the bugs. We then use the change information to look for both potential problematic parts and dynamics in development that can cause maintenance implications. We believe that the results of our study can help better manage Android software development.","PeriodicalId":383774,"journal":{"name":"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123816009","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 : 2012-06-02DOI: 10.1109/MSR.2012.6224302
Lucia, Ferdian Thung, D. Lo, Lingxiao Jiang
Many fault localization techniques have been proposed to facilitate debugging activities. Most of them attempt to pinpoint the location of faults (i.e., localize faults) based on a set of failing and correct executions and expect debuggers to investigate a certain number of located program elements to find faults. These techniques thus assume that faults are localizable, i.e., only one or a few lines of code that are close to one another are responsible for each fault. However, in reality, are faults localizable? In this work, we investigate hundreds of real faults in several software systems, and find that many faults may not be localizable to a few lines of code and these include faults with high severity level.
{"title":"Are faults localizable?","authors":"Lucia, Ferdian Thung, D. Lo, Lingxiao Jiang","doi":"10.1109/MSR.2012.6224302","DOIUrl":"https://doi.org/10.1109/MSR.2012.6224302","url":null,"abstract":"Many fault localization techniques have been proposed to facilitate debugging activities. Most of them attempt to pinpoint the location of faults (i.e., localize faults) based on a set of failing and correct executions and expect debuggers to investigate a certain number of located program elements to find faults. These techniques thus assume that faults are localizable, i.e., only one or a few lines of code that are close to one another are responsible for each fault. However, in reality, are faults localizable? In this work, we investigate hundreds of real faults in several software systems, and find that many faults may not be localizable to a few lines of code and these include faults with high severity level.","PeriodicalId":383774,"journal":{"name":"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130658343","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 : 2012-06-02DOI: 10.1109/MSR.2012.6224276
Jinqiu Yang, Lin Tan
Code search is an integral part of software development and program comprehension. The difficulty of code search lies in the inability to guess the exact words used in the code. Therefore, it is crucial for keyword-based code search to expand queries with semantically related words, e.g., synonyms and abbreviations, to increase the search effectiveness. However, it is limited to rely on resources such as English dictionaries and WordNet to obtain semantically related words in software, because many words that are semantically related in software are not semantically related in English. This paper proposes a simple and general technique to automatically infer semantically related words in software by leveraging the context of words in comments and code. We achieve a reasonable accuracy in seven large and popular code bases written in C and Java. Our further evaluation against the state of art shows that our technique can achieve a higher precision and recall.
{"title":"Inferring semantically related words from software context","authors":"Jinqiu Yang, Lin Tan","doi":"10.1109/MSR.2012.6224276","DOIUrl":"https://doi.org/10.1109/MSR.2012.6224276","url":null,"abstract":"Code search is an integral part of software development and program comprehension. The difficulty of code search lies in the inability to guess the exact words used in the code. Therefore, it is crucial for keyword-based code search to expand queries with semantically related words, e.g., synonyms and abbreviations, to increase the search effectiveness. However, it is limited to rely on resources such as English dictionaries and WordNet to obtain semantically related words in software, because many words that are semantically related in software are not semantically related in English. This paper proposes a simple and general technique to automatically infer semantically related words in software by leveraging the context of words in comments and code. We achieve a reasonable accuracy in seven large and popular code bases written in C and Java. Our further evaluation against the state of art shows that our technique can achieve a higher precision and recall.","PeriodicalId":383774,"journal":{"name":"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134124024","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 : 2012-06-02DOI: 10.1109/MSR.2012.6224270
Wei Hu, Dan Han, Abram Hindle, Kenny Wong
Android is an operating system designed specifically for mobile devices. It has a layered architecture. In this paper, we extract Android's concrete layered architecture by analyzing the build dependency relation between Android sub-projects and use it to validate the proposed conceptual architecture. Our experiment shows that Android's concrete architecture conforms to the conceptual architecture. Apart from that, we also show the extracted architecture can help developers and users better understand the Android system and further demonstrate its potential benefits in studying the impact of changes.
{"title":"The build dependency perspective of Android's concrete architecture","authors":"Wei Hu, Dan Han, Abram Hindle, Kenny Wong","doi":"10.1109/MSR.2012.6224270","DOIUrl":"https://doi.org/10.1109/MSR.2012.6224270","url":null,"abstract":"Android is an operating system designed specifically for mobile devices. It has a layered architecture. In this paper, we extract Android's concrete layered architecture by analyzing the build dependency relation between Android sub-projects and use it to validate the proposed conceptual architecture. Our experiment shows that Android's concrete architecture conforms to the conceptual architecture. Apart from that, we also show the extracted architecture can help developers and users better understand the Android system and further demonstrate its potential benefits in studying the impact of changes.","PeriodicalId":383774,"journal":{"name":"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125255076","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 : 2012-06-01DOI: 10.1109/MSR.2012.6224295
Daryl Posnett, Premkumar T. Devanbu, V. Filkov
Empirical software engineering researchers are concerned with understanding the relationships between outcomes of interest, e.g. defects, and process and product measures. The use of correlations to uncover strong relationships is a natural precursor to multivariate modeling. Unfortunately, correlation coefficients can be difficult and/or misleading to interpret. For example, a strong correlation occurs between variables that stand in a polynomial relationship; this may lead one mistakenly, and eventually misleadingly, to model a polynomially related variable in a linear regression. Likewise, a non-monotonic functional, or even non-functional relationship might be entirely missed by a correlation coefficient. Outliers can influence standard correlation measures, tied values can unduly influence even robust non-parametric rank correlation, measures, and smaller sample sizes can cause instability in correlation measures. A new bivariate measure of association, Maximal Information Coefficient (MIC) [1], promises to simultaneously discover if two variables have: a) any association, b) a functional relationship, and c) a nonlinear relationship. The MIC is a very useful complement to standard and rank correlation measures. It separately characterizes the existence of a relationship and its precise nature; thus, it enables more informed choices in modeling non-functional and nonlinear relationships, and a more nuanced indicator of potential problems with the values reported by standard and rank correlation measures. We illustrate the use of MIC using a variety of software engineering metrics. We study and explain the distributional properties of MIC and related measures in software engineering data, and illustrate the value of these measures for the empirical software engineering researcher.
{"title":"MIC check: A correlation tactic for ESE data","authors":"Daryl Posnett, Premkumar T. Devanbu, V. Filkov","doi":"10.1109/MSR.2012.6224295","DOIUrl":"https://doi.org/10.1109/MSR.2012.6224295","url":null,"abstract":"Empirical software engineering researchers are concerned with understanding the relationships between outcomes of interest, e.g. defects, and process and product measures. The use of correlations to uncover strong relationships is a natural precursor to multivariate modeling. Unfortunately, correlation coefficients can be difficult and/or misleading to interpret. For example, a strong correlation occurs between variables that stand in a polynomial relationship; this may lead one mistakenly, and eventually misleadingly, to model a polynomially related variable in a linear regression. Likewise, a non-monotonic functional, or even non-functional relationship might be entirely missed by a correlation coefficient. Outliers can influence standard correlation measures, tied values can unduly influence even robust non-parametric rank correlation, measures, and smaller sample sizes can cause instability in correlation measures. A new bivariate measure of association, Maximal Information Coefficient (MIC) [1], promises to simultaneously discover if two variables have: a) any association, b) a functional relationship, and c) a nonlinear relationship. The MIC is a very useful complement to standard and rank correlation measures. It separately characterizes the existence of a relationship and its precise nature; thus, it enables more informed choices in modeling non-functional and nonlinear relationships, and a more nuanced indicator of potential problems with the values reported by standard and rank correlation measures. We illustrate the use of MIC using a variety of software engineering metrics. We study and explain the distributional properties of MIC and related measures in software engineering data, and illustrate the value of these measures for the empirical software engineering researcher.","PeriodicalId":383774,"journal":{"name":"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115430151","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 : 2012-06-01DOI: 10.1109/MSR.2012.6224282
Aigerim Issabayeva, Ariadi Nugroho, Joost Visser
Software maintenance tasks are mainly related to fixing defects and implementing new features. Higher efficiency in performing such tasks is therefore going to reduce the costs of maintenance. A previous study involving open source systems has shown that higher software maintainability corresponds to faster speed in fixing defects [1]. In this paper we replicate the previous study by mining bug report data of three proprietary software projects. In one of the projects, a correlation between higher software maintainability and faster defect resolution is confirmed. The quality of issue handling process (e.g., issue registration accuracy and completeness, scope and complexity of issue workflow) should be considered in further research as it might explain the circumstances under which the correlation can be observed.
{"title":"Issue handling performance in proprietary software projects","authors":"Aigerim Issabayeva, Ariadi Nugroho, Joost Visser","doi":"10.1109/MSR.2012.6224282","DOIUrl":"https://doi.org/10.1109/MSR.2012.6224282","url":null,"abstract":"Software maintenance tasks are mainly related to fixing defects and implementing new features. Higher efficiency in performing such tasks is therefore going to reduce the costs of maintenance. A previous study involving open source systems has shown that higher software maintainability corresponds to faster speed in fixing defects [1]. In this paper we replicate the previous study by mining bug report data of three proprietary software projects. In one of the projects, a correlation between higher software maintainability and faster defect resolution is confirmed. The quality of issue handling process (e.g., issue registration accuracy and completeness, scope and complexity of issue workflow) should be considered in further research as it might explain the circumstances under which the correlation can be observed.","PeriodicalId":383774,"journal":{"name":"2012 9th IEEE Working Conference on Mining Software Repositories (MSR)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133187456","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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