Applying Social Network Analysis to Software Fault-Proneness Prediction

[1]  Witold Pedrycz,et al.  A comparative analysis of the efficiency of change metrics and static code attributes for defect prediction , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[2]  Taghi M. Khoshgoftaar,et al.  Unsupervised learning for expert-based software quality estimation , 2004, Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings..

[3]  Arvinder Kaur,et al.  Empirical validation of object-oriented metrics for predicting fault proneness models , 2010, Software Quality Journal.

[4]  James D. Herbsleb,et al.  Coordination Breakdowns and Their Impact on Development Productivity and Software Failures , 2013, IEEE Transactions on Software Engineering.

[5]  Hongyu Zhang,et al.  An investigation of the relationships between lines of code and defects , 2009, 2009 IEEE International Conference on Software Maintenance.

[6]  Lars Lundberg,et al.  Statistical models vs. expert estimation for fault prediction in modified code - an industrial case study , 2007, J. Syst. Softw..

[7]  Osamu Mizuno,et al.  Training on errors experiment to detect fault-prone software modules by spam filter , 2007, ESEC-FSE '07.

[8]  Ken-ichi Matsumoto,et al.  Accelerating cross-project knowledge collaboration using collaborative filtering and social networks , 2005, MSR.

[9]  Laurie A. Williams,et al.  Evaluating Complexity, Code Churn, and Developer Activity Metrics as Indicators of Software Vulnerabilities , 2011, IEEE Transactions on Software Engineering.

[10]  Pierfrancesco Bellini,et al.  Comparing fault-proneness estimation models , 2005, 10th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS'05).

[11]  Tibor Gyimóthy,et al.  New Conceptual Coupling and Cohesion Metrics for Object-Oriented Systems , 2010, 2010 10th IEEE Working Conference on Source Code Analysis and Manipulation.

[12]  Osamu Mizuno,et al.  Fault-prone module detection using large-scale text features based on spam filtering , 2010, Empirical Software Engineering.

[13]  Victor R. Basili,et al.  An Empirical Study of a Syntactic Complexity Family , 1983, IEEE Transactions on Software Engineering.

[14]  Norman E. Fenton,et al.  Software Measurement: Uncertainty and Causal Modeling , 2002, IEEE Softw..

[15]  Giovanni Denaro,et al.  An empirical evaluation of fault-proneness models , 2002, ICSE '02.

[16]  Premkumar T. Devanbu,et al.  Recalling the "imprecision" of cross-project defect prediction , 2012, SIGSOFT FSE.

[17]  Arwin Halim Predict fault-prone classes using the complexity of UML class diagram , 2013, 2013 International Conference on Computer, Control, Informatics and Its Applications (IC3INA).

[18]  Taghi M. Khoshgoftaar,et al.  Using neural networks to predict software faults during testing , 1996, IEEE Trans. Reliab..

[19]  Victor R. Basili,et al.  A Validation of Object-Oriented Design Metrics as Quality Indicators , 1996, IEEE Trans. Software Eng..

[20]  Tim Menzies,et al.  Better cross company defect prediction , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[21]  Kevin Crowston,et al.  Social dynamics of free and open source team communications , 2006, OSS.

[22]  M KhoshgoftaarTaghi,et al.  Classification of Fault-Prone Software Modules , 1998 .

[23]  Qian Yin,et al.  Software quality prediction using Affinity Propagation algorithm , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[24]  Iker Gondra,et al.  Applying machine learning to software fault-proneness prediction , 2008, J. Syst. Softw..

[25]  Ryouei Takahashi,et al.  Software quality classification model based on McCabe's complexity measure , 1997, J. Syst. Softw..

[26]  Vladimir Batagelj,et al.  Centrality in Social Networks , 1993 .

[27]  Fangjun Wu Empirical Validation of Object-Oriented Metrics on NASA for Fault Prediction , 2011 .

[28]  Braden Simpson Changeset based developer communication to detect software failures , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[29]  Taghi M. Khoshgoftaar,et al.  Evolutionary neural networks: a robust approach to software reliability problems , 1997, Proceedings The Eighth International Symposium on Software Reliability Engineering.

[30]  Jerry L. Trahan,et al.  Neural-network techniques for software-quality evaluation , 1998, Annual Reliability and Maintainability Symposium. 1998 Proceedings. International Symposium on Product Quality and Integrity.

[31]  Laurie A. Williams,et al.  Socio-technical developer networks: should we trust our measurements? , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[32]  Xin Zheng,et al.  Empirically Validating Software Metrics for Risk Prediction Based on Intelligent Methods , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[33]  Sandro Morasca,et al.  A hybrid approach to analyze empirical software engineering data and its application to predict module fault-proneness in maintenance , 2000, J. Syst. Softw..

[34]  M. Lipow,et al.  Number of Faults per Line of Code , 1982, IEEE Transactions on Software Engineering.

[35]  Sandro Morasca,et al.  Deriving models of software fault-proneness , 2002, SEKE '02.

[36]  Banu Diri,et al.  Practical development of an Eclipse-based software fault prediction tool using Naive Bayes algorithm , 2011, Expert Syst. Appl..

[37]  Sandro Morasca,et al.  Defining and Validating Measures for Object-Based High-Level Design , 1999, IEEE Trans. Software Eng..

[38]  Yuming Zhou,et al.  Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults , 2006, IEEE Transactions on Software Engineering.

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

[40]  Yu Qi,et al.  Source code-based software risk assessing , 2005, SAC '05.

[41]  Nachiappan Nagappan,et al.  Predicting Subsystem Failures using Dependency Graph Complexities , 2007, The 18th IEEE International Symposium on Software Reliability (ISSRE '07).

[42]  Jehad Al Dallal The impact of accounting for special methods in the measurement of object-oriented class cohesion on refactoring and fault prediction activities , 2012, J. Syst. Softw..

[43]  Shinji Kusumoto,et al.  Prediction of fault-proneness at early phase in object-oriented development , 1999, Proceedings 2nd IEEE International Symposium on Object-Oriented Real-Time Distributed Computing (ISORC'99) (Cat. No.99-61702).

[44]  Atul Gupta,et al.  Validating the Effectiveness of Object-Oriented Metrics over Multiple Releases for Predicting Fault Proneness , 2012, 2012 19th Asia-Pacific Software Engineering Conference.

[45]  Daniela E. Damian,et al.  Predicting build failures using social network analysis on developer communication , 2009, 2009 IEEE 31st International Conference on Software Engineering.

[46]  R. Hanneman Introduction to Social Network Methods , 2001 .

[47]  Abhijit S. Pandya,et al.  A comparative study of pattern recognition techniques for quality evaluation of telecommunications software , 1994, IEEE J. Sel. Areas Commun..

[48]  E.J. Weyuker,et al.  Using Developer Information as a Factor for Fault Prediction , 2007, Third International Workshop on Predictor Models in Software Engineering (PROMISE'07: ICSE Workshops 2007).

[49]  Jeff Tian,et al.  A comparison of measurement and defect characteristics of new and legacy software systems , 1998, J. Syst. Softw..

[50]  Abhijit S. Pandya,et al.  Application of neural networks for predicting program faults , 1995, Ann. Softw. Eng..

[51]  Olcay Taner Yildiz,et al.  Software defect prediction using Bayesian networks , 2012, Empirical Software Engineering.

[52]  Mei-Hwa Chen,et al.  An empirical study on object-oriented metrics , 1999, Proceedings Sixth International Software Metrics Symposium (Cat. No.PR00403).

[53]  Andreas Zeller,et al.  Predicting defects in SAP Java code: An experience report , 2009, 2009 31st International Conference on Software Engineering - Companion Volume.

[54]  Lucas Layman,et al.  Iterative identification of fault-prone binaries using in-process metrics , 2008, ESEM '08.

[55]  Tohru Kikuno,et al.  Fault-Prone Filtering: Detection of Fault-Prone Modules Using Spam Filtering Technique , 2007, ESEM 2007.

[56]  Richard Torkar,et al.  Software fault prediction metrics: A systematic literature review , 2013, Inf. Softw. Technol..

[57]  Bojan Cukic,et al.  Predicting fault prone modules by the Dempster-Shafer belief networks , 2003, 18th IEEE International Conference on Automated Software Engineering, 2003. Proceedings..

[58]  Sallie M. Henry,et al.  Software Structure Metrics Based on Information Flow , 1981, IEEE Transactions on Software Engineering.

[59]  Banu Diri,et al.  Clustering and Metrics Thresholds Based Software Fault Prediction of Unlabeled Program Modules , 2009, 2009 Sixth International Conference on Information Technology: New Generations.

[60]  Bhekisipho Twala,et al.  Predicting Software Faults in Large Space Systems using Machine Learning Techniques , 2011 .

[61]  Victor R. Basili,et al.  Developing Interpretable Models with Optimized Set Reduction for Identifying High-Risk Software Components , 1993, IEEE Trans. Software Eng..

[62]  Lionel C. Briand,et al.  A Unified Framework for Cohesion Measurement , 1997, IEEE METRICS.

[63]  Ronald Rousseau,et al.  Social network analysis: a powerful strategy, also for the information sciences , 2002, J. Inf. Sci..

[64]  Walcélio L. Melo,et al.  Polymorphism measures for early risk prediction , 1999, Proceedings of the 1999 International Conference on Software Engineering (IEEE Cat. No.99CB37002).

[65]  Markus Pizka,et al.  Concise and consistent naming , 2005, 13th International Workshop on Program Comprehension (IWPC'05).

[66]  Satwinder Singh,et al.  Effectiveness of encapsulation and object-oriented metrics to refactor code and identify error prone classes using bad smells , 2011, SOEN.

[67]  Jordan Ell,et al.  Identifying failure inducing developer pairs within developer networks , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[68]  Elaine J. Weyuker,et al.  Do too many cooks spoil the broth? Using the number of developers to enhance defect prediction models , 2008, Empirical Software Engineering.

[69]  Victor R. Basili,et al.  Software errors and complexity: an empirical investigation0 , 1984, CACM.

[70]  Chisu Wu,et al.  Criticality prediction models using SDL metrics set , 1997, Proceedings of Joint 4th International Computer Science Conference and 4th Asia Pacific Software Engineering Conference.

[71]  Andreas Zeller,et al.  Mining metrics to predict component failures , 2006, ICSE.

[72]  Rachel Harrison,et al.  A study of subgroup discovery approaches for defect prediction , 2013, Inf. Softw. Technol..

[73]  Lionel C. Briand,et al.  A Unified Framework for Coupling Measurement in Object-Oriented Systems , 1999, IEEE Trans. Software Eng..

[74]  Hausi A. Müller,et al.  Predicting fault-proneness using OO metrics. An industrial case study , 2002, Proceedings of the Sixth European Conference on Software Maintenance and Reengineering.

[75]  Foutse Khomh,et al.  Mining the relationship between anti-patterns dependencies and fault-proneness , 2013, 2013 20th Working Conference on Reverse Engineering (WCRE).

[76]  Elaine J. Weyuker,et al.  Does measuring code change improve fault prediction? , 2011, Promise '11.

[77]  John C. Munson,et al.  The effects of fault counting methods on fault model quality , 2004, Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004..

[78]  Osamu Mizuno,et al.  Spam Filter Based Approach for Finding Fault-Prone Software Modules , 2007, Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007).

[79]  Jin Zhao,et al.  Applying statistical methodology to optimize and simplify software metric models with missing data , 2006, SAC.

[80]  Yue Jiang,et al.  Variance Analysis in Software Fault Prediction Models , 2009, 2009 20th International Symposium on Software Reliability Engineering.

[81]  Harald C. Gall,et al.  Cross-project defect prediction: a large scale experiment on data vs. domain vs. process , 2009, ESEC/SIGSOFT FSE.

[82]  Sebastian G. Elbaum,et al.  Code churn: a measure for estimating the impact of code change , 1998, Proceedings. International Conference on Software Maintenance (Cat. No. 98CB36272).

[83]  Zsuzsanna Marian,et al.  Software defect prediction using relational association rule mining , 2014, Inf. Sci..

[84]  Letha H. Etzkorn,et al.  Empirical Validation of Three Software Metrics Suites to Predict Fault-Proneness of Object-Oriented Classes Developed Using Highly Iterative or Agile Software Development Processes , 2007, IEEE Transactions on Software Engineering.

[85]  M KhoshgoftaarTaghi,et al.  Comparative Assessment of Software Quality Classification Techniques , 2004 .

[86]  Bart Baesens,et al.  Toward Comprehensible Software Fault Prediction Models Using Bayesian Network Classifiers , 2013, IEEE Transactions on Software Engineering.

[87]  Ulrik Brandes,et al.  Network Analysis: Methodological Foundations , 2010 .

[88]  Qinbao Song,et al.  A General Software Defect-Proneness Prediction Framework , 2011, IEEE Transactions on Software Engineering.

[89]  Stephen R. Schach,et al.  Prediction of Run-Time Failures Using Static Product Quality Metrics , 2004, Software Quality Journal.

[90]  Ayse Basar Bener,et al.  On the relative value of cross-company and within-company data for defect prediction , 2009, Empirical Software Engineering.

[91]  Taghi M. Khoshgoftaar,et al.  Count Models for Software Quality Estimation , 2007, IEEE Transactions on Reliability.

[92]  Jehad Al Dallal Fault prediction and the discriminative powers of connectivity-based object-oriented class cohesion metrics , 2012, Inf. Softw. Technol..

[93]  David P. Darcy,et al.  Managerial Use of Metrics for Object-Oriented Software: An Exploratory Analysis , 1998, IEEE Trans. Software Eng..

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

[95]  Nachiappan Nagappan,et al.  Predicting defects using network analysis on dependency graphs , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[96]  Ahmed E. Hassan,et al.  Predicting faults using the complexity of code changes , 2009, 2009 IEEE 31st International Conference on Software Engineering.

[97]  Bruce Christianson,et al.  The misuse of the NASA metrics data program data sets for automated software defect prediction , 2011, EASE.

[98]  Martin J. Shepperd,et al.  Comparing Software Prediction Techniques Using Simulation , 2001, IEEE Trans. Software Eng..

[99]  Jin Liu,et al.  Dictionary learning based software defect prediction , 2014, ICSE.

[100]  Taghi M. Khoshgoftaar,et al.  The Detection of Fault-Prone Programs , 1992, IEEE Trans. Software Eng..

[101]  Taghi M. Khoshgoftaar,et al.  Early Quality Prediction: A Case Study in Telecommunications , 1996, IEEE Softw..

[102]  Rishab Aiyer Ghosh Clustering and dependencies in free/open source software development: Methodology and tools , 2003, First Monday.

[103]  Richard C. Holt,et al.  The top ten list: dynamic fault prediction , 2005, 21st IEEE International Conference on Software Maintenance (ICSM'05).

[104]  Wei Hu,et al.  Using citation influence to predict software defects , 2013, 2013 10th Working Conference on Mining Software Repositories (MSR).

[105]  Steffen Herbold,et al.  Training data selection for cross-project defect prediction , 2013, PROMISE.

[106]  Marco Tulio Valente,et al.  Predicting software defects with causality tests , 2014, J. Syst. Softw..

[107]  P. Bonacich Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

[108]  Lars Lundberg,et al.  Improving Fault Detection in Modified Code — A Study from the Telecommunication Industry , 2007, Journal of Computer Science and Technology.

[109]  Harald C. Gall,et al.  Putting It All Together: Using Socio-technical Networks to Predict Failures , 2009, 2009 20th International Symposium on Software Reliability Engineering.

[110]  Taghi M. Khoshgoftaar,et al.  A Comprehensive Empirical Study of Count Models for Software Fault Prediction , 2007, IEEE Transactions on Reliability.

[111]  Bora Caglayan,et al.  Defect prediction using social network analysis on issue repositories , 2011, ICSSP '11.

[112]  A.D. Oral,et al.  Defect prediction for embedded software , 2007, 2007 22nd international symposium on computer and information sciences.

[113]  T. Zimmermann,et al.  Predicting Faults from Cached History , 2007, 29th International Conference on Software Engineering (ICSE'07).

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

[115]  Banu Diri,et al.  Metrics-Driven Software Quality Prediction Without Prior Fault Data , 2010 .

[116]  Bojan Cukic,et al.  Replacing code metrics in software fault prediction with early life cycle metrics , 2013, 2013 IEEE Third International Conference on Information Science and Technology (ICIST).

[117]  Martin Pinzger,et al.  Method-level bug prediction , 2012, Proceedings of the 2012 ACM-IEEE International Symposium on Empirical Software Engineering and Measurement.

[118]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[119]  Yue Jiang,et al.  Fault Prediction using Early Lifecycle Data , 2007, The 18th IEEE International Symposium on Software Reliability (ISSRE '07).

[120]  Taghi M. Khoshgoftaar,et al.  Software Quality Analysis of Unlabeled Program Modules With Semisupervised Clustering , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[121]  Swapna S. Gokhale,et al.  Static and dynamic distance metrics for feature-based code analysis , 2005, J. Syst. Softw..

[122]  A. Gupta,et al.  Investigating object-oriented design metrics to predict fault-proneness of software modules , 2012, 2012 CSI Sixth International Conference on Software Engineering (CONSEG).

[123]  Joanne Bechta Dugan,et al.  Empirical Analysis of Software Fault Content and Fault Proneness Using Bayesian Methods , 2007, IEEE Transactions on Software Engineering.

[124]  Daniela E. Damian,et al.  Does Socio-Technical Congruence Have an Effect on Software Build Success? A Study of Coordination in a Software Project , 2011, IEEE Transactions on Software Engineering.

[125]  Tibor Gyimóthy,et al.  Empirical validation of object-oriented metrics on open source software for fault prediction , 2005, IEEE Transactions on Software Engineering.

[126]  Akif Günes Koru,et al.  Comparing high-change modules and modules with the highest measurement values in two large-scale open-source products , 2005, IEEE Transactions on Software Engineering.

[127]  Karim O. Elish,et al.  Predicting defect-prone software modules using support vector machines , 2008, J. Syst. Softw..

[128]  David W. Binkley,et al.  Increasing diversity: Natural language measures for software fault prediction , 2009, J. Syst. Softw..

[129]  Foutse Khomh,et al.  Predicting Bugs Using Antipatterns , 2013, 2013 IEEE International Conference on Software Maintenance.

[130]  Lionel C. Briand,et al.  Predicting fault-prone components in a java legacy system , 2006, ISESE '06.

[131]  Yuming Zhou,et al.  On the ability of complexity metrics to predict fault-prone classes in object-oriented systems , 2010, J. Syst. Softw..

[132]  Javam C. Machado,et al.  The prediction of faulty classes using object-oriented design metrics , 2001, J. Syst. Softw..

[133]  Tracy Hall,et al.  Researcher Bias: The Use of Machine Learning in Software Defect Prediction , 2014, IEEE Transactions on Software Engineering.

[134]  Ingunn Myrtveit,et al.  Reliability and validity in comparative studies of software prediction models , 2005, IEEE Transactions on Software Engineering.

[135]  Martin Shepperd,et al.  Derivation and Validation of Software Metrics , 1993 .

[136]  Abraham Bernstein,et al.  Predicting defect densities in source code files with decision tree learners , 2006, MSR '06.

[137]  C. Spearman The proof and measurement of association between two things. By C. Spearman, 1904. , 1987, The American journal of psychology.

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

[139]  A. Kaur,et al.  Application of Random Forest in Predicting Fault-Prone Classes , 2008, 2008 International Conference on Advanced Computer Theory and Engineering.

[140]  Taghi M. Khoshgoftaar,et al.  Using the genetic algorithm to build optimal neural networks for fault-prone module detection , 1996, Proceedings of ISSRE '96: 7th International Symposium on Software Reliability Engineering.

[141]  Hongfang Liu,et al.  An Investigation into the Functional Form of the Size-Defect Relationship for Software Modules , 2009, IEEE Transactions on Software Engineering.

[142]  Daniela Cruzes,et al.  A study of cyclic dependencies on defect profile of software components , 2013, J. Syst. Softw..

[143]  Arvinder Kaur,et al.  Empirical analysis for investigating the effect of object-oriented metrics on fault proneness: a replicated case study , 2009 .

[144]  Stephen G. MacDonell,et al.  Data quality in empirical software engineering: a targeted review , 2013, EASE '13.

[145]  Michalis Faloutsos,et al.  Graph-based analysis and prediction for software evolution , 2012, 2012 34th International Conference on Software Engineering (ICSE).

[146]  Ahmed E. Hassan,et al.  Understanding the impact of code and process metrics on post-release defects: a case study on the Eclipse project , 2010, ESEM '10.

[147]  Chris F. Kemerer,et al.  A Metrics Suite for Object Oriented Design , 2015, IEEE Trans. Software Eng..

[148]  Akif Günes Koru,et al.  An empirical comparison and characterization of high defect and high complexity modules , 2003, J. Syst. Softw..

[149]  Hareton K. N. Leung,et al.  An in-depth study of the potentially confounding effect of class size in fault prediction , 2014, TSEM.

[150]  Tracy Hall,et al.  A Systematic Literature Review on Fault Prediction Performance in Software Engineering , 2012, IEEE Transactions on Software Engineering.

[151]  James F. Power,et al.  Run-Time Cohesion Metrics: An Empirical Investigation , 2004, Software Engineering Research and Practice.

[152]  Michel R. V. Chaudron,et al.  Assessing UML design metrics for predicting fault-prone classes in a Java system , 2010, 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010).

[153]  Osamu Mizuno,et al.  A Cross-Project Evaluation of Text-Based Fault-Prone Module Prediction , 2014, 2014 6th International Workshop on Empirical Software Engineering in Practice.

[154]  Audris Mockus,et al.  Organizational volatility and its effects on software defects , 2010, FSE '10.

[155]  Michael R. Lyu,et al.  A novel method for early software quality prediction based on support vector machine , 2005, 16th IEEE International Symposium on Software Reliability Engineering (ISSRE'05).

[156]  Bart Goethals,et al.  Predicting the severity of a reported bug , 2010, 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010).

[157]  Taghi M. Khoshgoftaar,et al.  Using product, process, and execution metrics to predict fault-prone software modules with classification trees , 2000, Proceedings. Fifth IEEE International Symposium on High Assurance Systems Engineering (HASE 2000).

[158]  Mohammad Zulkernine,et al.  Using complexity, coupling, and cohesion metrics as early indicators of vulnerabilities , 2011, J. Syst. Archit..

[159]  Elaine J. Weyuker,et al.  Predicting the location and number of faults in large software systems , 2005, IEEE Transactions on Software Engineering.

[160]  Taghi M. Khoshgoftaar,et al.  Empirical Assessment of a Software Metric: The Information Content of Operators , 2004, Software Quality Journal.

[161]  Hirohisa Aman An Empirical Analysis on Fault-Proneness of Well-Commented Modules , 2012, 2012 Fourth International Workshop on Empirical Software Engineering in Practice.

[162]  Ye Yang,et al.  An investigation on the feasibility of cross-project defect prediction , 2012, Automated Software Engineering.

[163]  Elaine J. Weyuker,et al.  Programmer-based fault prediction , 2010, PROMISE '10.

[164]  N. Nagappan,et al.  Use of relative code churn measures to predict system defect density , 2005, Proceedings. 27th International Conference on Software Engineering, 2005. ICSE 2005..

[165]  Elaine J. Weyuker,et al.  Comparing the effectiveness of several modeling methods for fault prediction , 2010, Empirical Software Engineering.

[166]  Taghi M. Khoshgoftaar,et al.  Application of neural networks to software quality modeling of a very large telecommunications system , 1997, IEEE Trans. Neural Networks.

[167]  Jin Xu,et al.  A Topological Analysis of the Open Souce Software Development Community , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[168]  Hirohisa Aman An Empirical Analysis of the Impact of Comment Statements on Fault-Proneness of Small-Size Module , 2012, 2012 19th Asia-Pacific Software Engineering Conference.

[169]  Victor R. Basili,et al.  The influence of organizational structure on software quality , 2008, 2008 ACM/IEEE 30th International Conference on Software Engineering.

[170]  Laurie A. Williams,et al.  Predicting failures with developer networks and social network analysis , 2008, SIGSOFT '08/FSE-16.

[171]  Ferat Sahin,et al.  In-Vivo Fault Analysis and Real-Time Fault Prediction for RF Generators Using State-of-the-Art Classifiers , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[172]  Lionel C. Briand,et al.  A systematic and comprehensive investigation of methods to build and evaluate fault prediction models , 2010, J. Syst. Softw..

[173]  Albert Endres An analysis of errors and their causes in system programs , 1975 .

[174]  Taghi M. Khoshgoftaar,et al.  Using Classification Trees for Software Quality Models: Lessons Learned , 1999, Int. J. Softw. Eng. Knowl. Eng..

[175]  Yi Zhang,et al.  Classifying Software Changes: Clean or Buggy? , 2008, IEEE Transactions on Software Engineering.

[176]  Tim Menzies,et al.  Data Mining Static Code Attributes to Learn Defect Predictors , 2007 .

[177]  Cong Jin,et al.  Quality prediction model of object-oriented software system using computational intelligence , 2009, 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS).

[178]  Harald C. Gall,et al.  Don't touch my code!: examining the effects of ownership on software quality , 2011, ESEC/FSE '11.

[179]  Michael English,et al.  Fault detection and prediction in an open-source software project , 2009, PROMISE '09.

[180]  Sallie M. Henry,et al.  Object-oriented metrics that predict maintainability , 1993, J. Syst. Softw..

[181]  Harvey P. Siy,et al.  Predicting Fault Incidence Using Software Change History , 2000, IEEE Trans. Software Eng..

[182]  Elaine J. Weyuker,et al.  On the use of calling structure information to improve fault prediction , 2011, Empirical Software Engineering.

[183]  Yann-Gaël Guéhéneuc,et al.  Can Lexicon Bad Smells Improve Fault Prediction? , 2012, 2012 19th Working Conference on Reverse Engineering.

[184]  Kapsu Kim,et al.  Identifying fault-prone function blocks using the neural networks - an empirical study , 1997, 1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM. 10 Years Networking the Pacific Rim, 1987-1997.

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

[186]  Neeraj Kumar Goyal,et al.  Predicting Fault-prone Software Module Using Data Mining Technique and Fuzzy Logic , 2010 .

[187]  Taghi M. Khoshgoftaar,et al.  Classification-tree models of software-quality over multiple releases , 2000, IEEE Trans. Reliab..

[188]  Elaine J. Weyuker,et al.  Looking for bugs in all the right places , 2006, ISSTA '06.

[189]  Parag C. Pendharkar,et al.  Exhaustive and heuristic search approaches for learning a software defect prediction model , 2010, Eng. Appl. Artif. Intell..

[190]  Elaine J. Weyuker,et al.  Where the bugs are , 2004, ISSTA '04.

[191]  Per Runeson,et al.  A Second Replicated Quantitative Analysis of Fault Distributions in Complex Software Systems , 2007, IEEE Transactions on Software Engineering.

[192]  Raed Shatnawi,et al.  The effectiveness of software metrics in identifying error-prone classes in post-release software evolution process , 2008, J. Syst. Softw..

[193]  Abraham Bernstein,et al.  Improving defect prediction using temporal features and non linear models , 2007, IWPSE '07.

[194]  Bojan Cukic,et al.  Robust prediction of fault-proneness by random forests , 2004, 15th International Symposium on Software Reliability Engineering.

[195]  Satwinder Singh,et al.  Identification of Error Prone Classes for Fault Prediction Using Object Oriented Metrics , 2011, ACC.

[196]  Barbara Paech,et al.  Exploring the relationship of a file's history and its fault-proneness: An empirical method and its application to open source programs , 2010, Inf. Softw. Technol..

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

[198]  H. F. Li,et al.  An Empirical Study of Software Metrics , 1987, IEEE Transactions on Software Engineering.

[199]  Tim Menzies,et al.  Class level fault prediction using software clustering , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[200]  Silvio Romero de Lemos Meira,et al.  Enhancing RBF-DDA Algorithm's Robustness: Neural Networks Applied to Prediction of Fault-Prone Software Modules , 2008, IFIP AI.

[201]  Sousuke Amasaki,et al.  A Bayesian belief network for assessing the likelihood of fault content , 2003, 14th International Symposium on Software Reliability Engineering, 2003. ISSRE 2003..

[202]  L. Williams,et al.  Toward the Use of Automated Static Analysis Alerts for Early Identification of Vulnerability- and Attack-prone Components , 2007, Second International Conference on Internet Monitoring and Protection (ICIMP 2007).

[203]  Tim Menzies,et al.  How good is your blind spot sampling policy , 2004, Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings..

[204]  Tong-Seng Quah,et al.  Application of neural networks for software quality prediction using object-oriented metrics , 2005, J. Syst. Softw..

[205]  T. Menzies,et al.  Metrics that matter , 2002, 27th Annual NASA Goddard/IEEE Software Engineering Workshop, 2002. Proceedings..