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Bug Prioritization to Facilitate Bug Report Triage

Journal of Computer Science & Technology 2012年第2期27卷 397-412页

作者： Jaweria Kanwal onaiza maqbool Department of Computer Science Quaid-i-Azam University

The large number of new bug reports received in bug repositories of software systems makes their management a challenging *** these reports manually is time consuming,and often results in delaying the resolution of important *** address this issue,a recommender may be developed which automatically prioritizes the new bug *** this paper,we propose and evaluate a classification based approach to build such a *** use the Na¨ ve Bayes and Support Vector Machine （SVM） classifiers,and present a comparison to evaluate which classifier performs better in terms of *** a bug report contains both categorical and text features,another evaluation we perform is to determine the combination of features that better determines the priority of a *** evaluate the bug priority recommender,we use precision and recall measures and also propose two new measures,Nearest False Negatives （NFN） and Nearest False Positives （NFP）,which provide insight into the results produced by precision and *** findings are that the results of SVM are better than the Na¨ ve Bayes algorithm for text features,whereas for categorical features,Na¨ ve Bayes performance is better than *** highest accuracy is achieved with SVM when categorical and text features are combined for training.

关键词： bug triaging bug priority classification mining bug repositories evaluation measures

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Improved binary similarity measures for software modularization

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Frontiers of Information Technology & Electronic Engineering 2017年第8期18卷 1082-1107页

作者： Rashid NASEEM Mustafa Bin Mat DERIS onaiza maqbool Jing-peng LI Sara SHAHZAD Habib SHAH Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja 86500 Malaysia Dcpartment of Computer Science Quaid-i-Azam University Islamabad45320 Pakistan Division of Computer Science and Mathematics University of Stirling Stirling FK9 4LA UI Department of Computer Science University of Peshawar Peshawar 25120 Pakistan SFaculty of Computer and Information Systems Islamic University Madina Madina POBox 170 KSA

Various binary similarity measures have been employed in clustering approaches to make homogeneous groups of similar entities in the data. These similarity measures are mostly based only on the presence or absence of features. Binary similarity measures have also been explored with different clustering approaches （e.g., agglomera- tive hierarchical clustering） for software modularization to make software systems understandable and manageable. Each similarity measure has its own strengths and weaknesses which improve and deteriorate the clustering results, respectively. We highlight the strengths of some well-known existing binary similarity measures for software mod- ularization. Furthermore, based on these existing similarity measures, we introduce several improved new binary similarity measures. Proofs of the correctness with illustration and a series of experiments are presented to evaluate the effectiveness of our new binary similarity measures.

关键词： Binary similarity measure Binary features Combination of measures Software modularization

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