Bug Priority Assessment in Cross-Project Context Using Entropy-Based Measure
作者： Meera Sharma , Madhu Kumari , V. B. Singh
DOI: 10.1007/978-981-15-5243-4_10
关键词:
摘要: Software users report bugs on the bug tracking system in a distributed environment with different levels of understanding and knowledge about software. As result this, software repository data are increasing day by noise uncertainty it. The present summary need to be handled, so that it should not affect performance learning strategies for attributes fix time predictions. Bug prioritization is process deciding sequence fixed. Wrong results unresolved important delayed release software, thus affecting quality evolution priority prediction requires historical training classifiers. However, such always available practice all In circumstances, designing models from other projects solution. This using testing two called cross-project prediction. We have used Shannon entropy measure addition severity weight this paper, we proposed machine classifiers predict reported context handling uncertainty. Results show improvement entropy-based over existing summary-based newly coming report.
springer.com
sci-hub.st 参考文章(21)
Meera Sharma, Punam Bedi, K.K. Chaturvedi, V.B. Singh, Predicting the priority of a reported bug using machine learning techniques and cross project validation intelligent systems design and applications. pp. 539- 545 ,(2012) , 10.1109/ISDA.2012.6416595
K. K. Chaturvedi, P. K. Kapur, Sameer Anand, V. B. Singh, Predicting the complexity of code changes using entropy based measures International Journal of Systems Assurance Engineering and Management. ,vol. 5, pp. 155- 164 ,(2014) , 10.1007/S13198-014-0226-5
Duksan Ryu, Okjoo Choi, Jongmoon Baik, Value-cognitive boosting with a support vector machine for cross-project defect prediction Empirical Software Engineering. ,vol. 21, pp. 43- 71 ,(2016) , 10.1007/S10664-014-9346-4
Yuan Tian, David Lo, Chengnian Sun, DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis international conference on software maintenance. pp. 200- 209 ,(2013) , 10.1109/ICSM.2013.31
Zhimin He, Fengdi Shu, Ye Yang, Mingshu Li, Qing Wang, An investigation on the feasibility of cross-project defect prediction automated software engineering. ,vol. 19, pp. 167- 199 ,(2012) , 10.1007/S10515-011-0090-3
Gerardo Canfora, Andrea De Lucia, Massimiliano Di Penta, Rocco Oliveto, Annibale Panichella, Sebastiano Panichella, Multi-objective Cross-Project Defect Prediction international conference on software testing verification and validation. pp. 252- 261 ,(2013) , 10.1109/ICST.2013.38
Meera Sharma, Punam Bedi, V. B. Singh, An empirical evaluation of cross project priority prediction International Journal of Systems Assurance Engineering and Management. ,vol. 5, pp. 651- 663 ,(2014) , 10.1007/S13198-014-0219-4
Ying Ma, Guangchun Luo, Xue Zeng, Aiguo Chen, Transfer learning for cross-company software defect prediction Information & Software Technology. ,vol. 54, pp. 248- 256 ,(2012) , 10.1016/J.INFSOF.2011.09.007
Jaechang Nam, Sinno Jialin Pan, Sunghun Kim, Transfer defect learning international conference on software engineering. pp. 382- 391 ,(2013) , 10.5555/2486788.2486839
Thomas Zimmermann, Nachiappan Nagappan, Harald Gall, Emanuel Giger, Brendan Murphy, Cross-project defect prediction: a large scale experiment on data vs. domain vs. process foundations of software engineering. pp. 91- 100 ,(2009) , 10.1145/1595696.1595713