On the value of a prioritization scheme for resolving Self-admitted technical debt
暂无分享,去创建一个
Jacky W. Keung | Jeffrey Svajlenko | Solomon Mensah | Qing Mi | Kwabena Ebo Bennin | J. Keung | Solomon Mensah | K. E. Bennin | Qing Mi | Jeffrey Svajlenko
[1] Fazli Can,et al. Information retrieval on Turkish texts , 2008 .
[2] Narayan Ramasubbu,et al. Managing Technical Debt in Enterprise Software Packages , 2014, IEEE Transactions on Software Engineering.
[3] CunninghamWard. The WyCash portfolio management system , 1992 .
[4] Robbie T. Nakatsu,et al. A taxonomy of crowdsourcing based on task complexity , 2014, J. Inf. Sci..
[5] Jan Bosch,et al. Architecture Technical Debt: Understanding Causes and a Qualitative Model , 2014, 2014 40th EUROMICRO Conference on Software Engineering and Advanced Applications.
[6] N. Cliff. Dominance statistics: Ordinal analyses to answer ordinal questions. , 1993 .
[7] Rong Jin,et al. Understanding bag-of-words model: a statistical framework , 2010, Int. J. Mach. Learn. Cybern..
[8] Emad Shihab,et al. Detecting and quantifying different types of self-admitted technical Debt , 2015, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD).
[9] Bashar Nuseibeh,et al. Signing Off: The State of the Journal , 2014, IEEE Trans. Software Eng..
[10] Tore Dybå,et al. A systematic review of effect size in software engineering experiments , 2007, Inf. Softw. Technol..
[11] Forrest Shull,et al. Investigating the impact of design debt on software quality , 2011, MTD '11.
[12] Emad Shihab,et al. Examining the Impact of Self-Admitted Technical Debt on Software Quality , 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER).
[13] Pearl Brereton,et al. Robust Statistical Methods for Empirical Software Engineering , 2017, Empirical Software Engineering.
[14] Claes Wohlin,et al. Experimentation in Software Engineering , 2012, Springer Berlin Heidelberg.
[15] Jonathan I. Maletic,et al. Lightweight Transformation and Fact Extraction with the srcML Toolkit , 2011, 2011 IEEE 11th International Working Conference on Source Code Analysis and Manipulation.
[16] Emad Shihab,et al. An Exploratory Study on Self-Admitted Technical Debt , 2014, 2014 IEEE International Conference on Software Maintenance and Evolution.
[17] ArcuriAndrea,et al. A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering , 2014 .
[18] Thomas Zimmermann,et al. Predicting Bugs from History , 2008, Software Evolution.
[19] Robert L. Nord,et al. Technical Debt: From Metaphor to Theory and Practice , 2012, IEEE Software.
[20] William J. J. Rey,et al. Robust statistical methods , 1978 .
[21] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[22] Norman E. Fenton,et al. Quantitative Analysis of Faults and Failures in a Complex Software System , 2000, IEEE Trans. Software Eng..
[23] Lionel C. Briand,et al. A Hitchhiker's guide to statistical tests for assessing randomized algorithms in software engineering , 2014, Softw. Test. Verification Reliab..
[24] Razvan C. Bunescu,et al. Learning to rank relevant files for bug reports using domain knowledge , 2014, SIGSOFT FSE.
[25] Harald C. Gall,et al. Do Code and Comments Co-Evolve? On the Relation between Source Code and Comment Changes , 2007, 14th Working Conference on Reverse Engineering (WCRE 2007).
[26] Juan Garbajosa,et al. A framework to aid in decision making for technical debt management , 2015, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD).
[27] Pierre-Yves Schobbens,et al. Towards statistical prioritization for software product lines testing , 2013, VaMoS.
[28] Peng Liang,et al. A systematic mapping study on technical debt and its management , 2015, J. Syst. Softw..
[29] Yuming Zhou,et al. Is Learning-to-Rank Cost-Effective in Recommending Relevant Files for Bug Localization? , 2015, 2015 IEEE International Conference on Software Quality, Reliability and Security.
[30] Gabriele Bavota,et al. A Large-Scale Empirical Study on Self-Admitted Technical Debt , 2016, 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR).
[31] Xingming Sun,et al. Toward Efficient Multi-Keyword Fuzzy Search Over Encrypted Outsourced Data With Accuracy Improvement , 2016, IEEE Transactions on Information Forensics and Security.
[32] Song Wang,et al. Will This Bug-Fixing Change Break Regression Testing? , 2015, 2015 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM).
[33] Jacky W. Keung,et al. Rework Effort Estimation of Self-admitted Technical Debt , 2016, QuASoQ/TDA@APSEC.
[34] Rafael Morales Bueno,et al. TF-SIDF: Term frequency, sketched inverse document frequency , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.
[35] Mário André de Freitas Farias,et al. A Contextualized Vocabulary Model for identifying technical debt on code comments , 2015, 2015 IEEE 7th International Workshop on Managing Technical Debt (MTD).
[36] Ward Cunningham,et al. The WyCash portfolio management system , 1992, OOPSLA '92.
[37] Danilo Caivano,et al. Does the level of detail of UML diagrams affect the maintainability of source code?: a family of experiments , 2016, Empirical Software Engineering.
[38] Yasutaka Kamei,et al. Defect Prediction: Accomplishments and Future Challenges , 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER).
[39] James M. Tien,et al. Progressive Random Sampling With Stratification , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).