#Jedit version 5.3 git hub verification
#Jedit version 5.3 git hub software

#Jedit version 5.3 git hub software

Springer, BerlinĬzibula G, Marian Z, Czibula IG (2014) Software defect prediction using relational association rule mining. IEEE Trans Softw Eng 20(6):476–493Ĭoello CC, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems. Softw Test Verif Reliab 25(4):426–459Ĭhidamber S, Kemerer C (1994) A metrics suite for object oriented design.

#Jedit version 5.3 git hub verification

In: 2013 IEEE sixth international conference on software testing, verification and validation (ICST), IEEE, pp 252–261Ĭanfora G, Lucia AD, Penta MD, Oliveto R, Panichella A, Panichella S (2015) Defect prediction as a multiobjective optimization problem. Softw Eng IEEE Trans 22(10):751–761Ĭanfora G, De Lucia A, Di Penta M, Oliveto R, Panichella A, Panichella S (2013) Multi-objective cross-project defect prediction. Our findings show that multi-objective logistic regression is more cost-effective than single-objective algorithms.īasili VR, Briand LC, Melo WL (1996) A validation of object-oriented design metrics as quality indicators. We have used 11 projects from the PROMISE repository consisting of a total of 41 different versions of these projects. The two multi-objective defect prediction models are compared with four traditional machine learning algorithms, namely logistic regression, naïve Bayes, decision tree and random forest.

In this paper, we formulate cross-version defect prediction problem as a multi-objective optimization problem with two objective functions: (a) maximizing recall by minimizing misclassification cost and (b) maximizing recall by minimizing cost of QA activities on defect prone files. This is more practical than the other two ways of building models, i.e., cross-project prediction model and cross- validation prediction models, as previous version of same software project will have similar parameter distribution among files. Cross-version defect prediction is building a prediction model from the previous version of a software project to predict defects in the current version. Software project teams can prioritize and put up rigorous quality assurance (QA) activities on these predicted defect-prone files to minimize post-release defects so that quality software can be delivered. Defect prediction models help software project teams to spot defect-prone source files of software systems.