A study on the changes of dynamic feature code when fixing bugs: towards the benefits and costs of Python dynamic features

[1]  Yuming Zhou,et al.  Towards an understanding of change types in bug fixing code , 2017, Inf. Softw. Technol..

[2]  Wanwangying Ma,et al.  Empirical analysis of network measures for predicting high severity software faults , 2016, Science China Information Sciences.

[3]  Baowen Xu,et al.  Python probabilistic type inference with natural language support , 2016, SIGSOFT FSE.

[4]  Baowen Xu,et al.  Python predictive analysis for bug detection , 2016, SIGSOFT FSE.

[5]  Baowen Xu,et al.  An Empirical Study on the Characteristics of Python Fine-Grained Source Code Change Types , 2016, 2016 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[6]  Baowen Xu,et al.  Tracking Down Dynamic Feature Code Changes against Python Software Evolution , 2016, 2016 Third International Conference on Trustworthy Systems and their Applications (TSA).

[7]  Ming Wen,et al.  Locus: Locating bugs from software changes , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).

[8]  Sukyoung Ryu,et al.  Battles with False Positives in Static Analysis of JavaScript Web Applications in the Wild , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C).

[9]  Tobias Wrigstad,et al.  Measuring polymorphism in python programs , 2015, DLS.

[10]  Zhendong Su,et al.  An Empirical Study on Real Bug Fixes , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[11]  Hong Mei,et al.  A survey on bug-report analysis , 2015, Science China Information Sciences.

[12]  Jim Baker,et al.  Design and evaluation of gradual typing for python , 2014, DLS.

[13]  Baowen Xu,et al.  Hybrid Information Flow Analysis for Python Bytecode , 2014, 2014 11th Web Information System and Application Conference.

[14]  Yi Sun,et al.  Some Code Smells Have a Significant but Small Effect on Faults , 2014, TSEM.

[15]  Yuming Zhou,et al.  Dynamic Slicing of Python Programs , 2014, 2014 IEEE 38th Annual Computer Software and Applications Conference.

[16]  Tobias Wrigstad,et al.  Tracing dynamic features in python programs , 2014, MSR 2014.

[17]  Lin Chen,et al.  Identifying extract class refactoring opportunities for internetware , 2014, Science China Information Sciences.

[18]  Gerardo Canfora,et al.  How changes affect software entropy: an empirical study , 2014, Empirical Software Engineering.

[19]  Baowen Xu,et al.  Static Slicing for Python First-Class Objects , 2013, 2013 13th International Conference on Quality Software.

[20]  Audris Mockus,et al.  A large-scale empirical study of just-in-time quality assurance , 2013, IEEE Transactions on Software Engineering.

[21]  Gail C. Murphy,et al.  Why did this code change? , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[22]  Baowen Xu,et al.  Finding shrink critical section refactoring opportunities for the evolution of concurrent code in trustworthy software , 2013, Science China Information Sciences.

[23]  G. Antoniol,et al.  An exploratory study of the impact of antipatterns on class change- and fault-proneness , 2012, Empirical Software Engineering.

[24]  Jan Vitek,et al.  The Eval That Men Do - A Large-Scale Study of the Use of Eval in JavaScript Applications , 2011, ECOOP.

[25]  Mira Mezini,et al.  Taming reflection: Aiding static analysis in the presence of reflection and custom class loaders , 2011, 2011 33rd International Conference on Software Engineering (ICSE).

[26]  Romain Robbes,et al.  How developers use the dynamic features of programming languages: the case of smalltalk , 2011, MSR '11.

[27]  Yanhong A. Liu,et al.  Alias analysis for optimization of dynamic languages , 2010, DLS '10.

[28]  Jan Vitek,et al.  An analysis of the dynamic behavior of JavaScript programs , 2010, PLDI '10.

[29]  Baowen Xu,et al.  A Constraint Based Bug Checking Approach for Python , 2009, 2009 33rd Annual IEEE International Computer Software and Applications Conference.

[30]  Sunghun Kim,et al.  Toward an understanding of bug fix patterns , 2009, Empirical Software Engineering.

[31]  Katsuhisa Maruyama,et al.  A change-aware development environment by recording editing operations of source code , 2008, MSR '08.

[32]  Andreas Zeller,et al.  Predicting faults from cached history , 2008, ISEC '08.

[33]  Harald C. Gall,et al.  Change Distilling:Tree Differencing for Fine-Grained Source Code Change Extraction , 2007, IEEE Transactions on Software Engineering.

[34]  Abraham Bernstein,et al.  Detecting similar Java classes using tree algorithms , 2006, MSR '06.

[35]  Miryung Kim,et al.  Program element matching for multi-version program analyses , 2006, MSR '06.

[36]  Dewayne E. Perry,et al.  Toward understanding the rhetoric of small source code changes , 2005, IEEE Transactions on Software Engineering.

[37]  Jeffrey S. Foster,et al.  Understanding source code evolution using abstract syntax tree matching , 2005, MSR.

[38]  Lucian Voinea,et al.  CVSscan: visualization of code evolution , 2005, SoftVis '05.

[39]  Oege de Moor,et al.  Measuring the dynamic behaviour of AspectJ programs , 2004, OOPSLA.

[40]  Alessandro Orso,et al.  A differencing algorithm for object-oriented programs , 2004, Proceedings. 19th International Conference on Automated Software Engineering, 2004..

[41]  David Leon,et al.  Dex: a semantic-graph differencing tool for studying changes in large code bases , 2004, 20th IEEE International Conference on Software Maintenance, 2004. Proceedings..

[42]  Harald C. Gall,et al.  Populating a Release History Database from version control and bug tracking systems , 2003, International Conference on Software Maintenance, 2003. ICSM 2003. Proceedings..

[43]  M F Sanner,et al.  Python: a programming language for software integration and development. , 1999, Journal of molecular graphics & modelling.

[44]  Susan Horwitz,et al.  Identifying the semantic and textual differences between two versions of a program , 1990, PLDI '90.

[45]  R. Fisher Statistical methods for research workers , 1927, Protoplasma.

[46]  Yang Feng,et al.  Mubug: a mobile service for rapid bug tracking , 2015, Science China Information Sciences.

[47]  James Harland,et al.  Evaluating the dynamic behaviour of Python applications , 2009, ACSC.

[48]  M. Kendall Statistical Methods for Research Workers , 1937, Nature.