An Exploratory Analysis on Software Developers' Bug-Introducing Tendency over Time

Understanding how software developers' erroneous tendency changes across time has significant implications for building fault-proneness prediction models and guiding software evolution testing. This paper initiates the investigation on software developers' bug-introducing tendency through an exploratory analysis. Five metrics are proposed to capture software developers' bug-introducing tendency and its correlated factors. A total of 76 software developers, working in four widely used software programs from GitHub, have been analyzed. The initial findings are presented.

[1]  Fuqun Huang,et al.  Systematically Improving Software Reliability: Considering Human Errors of Software Practitioners , 2011 .

[2]  Byoungju Choi,et al.  A family of code coverage-based heuristics for effective fault localization , 2010, J. Syst. Softw..

[3]  Gerald M. Weinberg,et al.  Psychology of computer programming , 1971 .

[4]  Dianxiang Xu,et al.  Towards Better Fault Localization: A Crosstab-Based Statistical Approach , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[5]  Akito Monden,et al.  An analysis of developer metrics for fault prediction , 2010, PROMISE '10.

[6]  Norman E. Fenton,et al.  A Critique of Software Defect Prediction Models , 1999, IEEE Trans. Software Eng..

[7]  Keith H. Bennett,et al.  Software maintenance and evolution: a roadmap , 2000, ICSE '00.

[8]  W. Eric Wong,et al.  The DStar Method for Effective Software Fault Localization , 2014, IEEE Transactions on Reliability.

[9]  Elaine J. Weyuker,et al.  The limited impact of individual developer data on software defect prediction , 2011, Empirical Software Engineering.

[10]  A. Fuqun Huang,et al.  A Taxonomy System to Identify Human Error Causes for Software Defects , 2012 .

[11]  Zhenyu Chen,et al.  Effective software fault localization using predicted execution results , 2015, Software Quality Journal.

[12]  Brendan Murphy,et al.  Can developer-module networks predict failures? , 2008, SIGSOFT '08/FSE-16.

[13]  Shou-Yu Lee,et al.  DRS: A Developer Risk Metric for Better Predicting Software Fault-Proneness , 2015, 2015 Second International Conference on Trustworthy Systems and Their Applications.

[14]  Rui Abreu,et al.  A Survey on Software Fault Localization , 2016, IEEE Transactions on Software Engineering.

[15]  Wang Yichen.  Review of Software Psychology , 2013 .

[16]  Bin Liu,et al.  The links between human error diversity and software diversity: Implications for fault diversity seeking , 2014, Sci. Comput. Program..

[17]  Fuqun Huang,et al.  Post-Completion Error in Software Development , 2016, 2016 IEEE/ACM Cooperative and Human Aspects of Software Engineering (CHASE).

[18]  Bin Liu,et al.  The impact of software process consistency on residual defects , 2015, J. Softw. Evol. Process..

[19]  LiuBin,et al.  The impact of software process consistency on residual defects , 2015 .

[20]  Bhavani M. Thuraisingham,et al.  Effective Software Fault Localization Using an RBF Neural Network , 2012, IEEE Transactions on Reliability.