Browsing by Author "Singh, V. B."
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Article Citation Count: 20Bug Severity Assessment in Cross Project Context and Identifying Training Candidates(World Scientific Publ Co Pte Ltd, 2017) Mısra, Sanjay; Misra, Sanjay; Sharma, Meera; Computer EngineeringThe automatic bug severity prediction will be useful in prioritising the development efforts, allocating resources and bug fixer. It needs historical data on which classifiers can be trained. In the absence of such historical data cross project prediction provides a good solution. In this paper, our objective is to automate the bug severity prediction by using a bug metric summary and to identify best training candidates in cross project context. The text mining technique has been used to extract the summary terms and trained the classifiers using these terms. About 63 training candidates have been designed by combining seven datasets of Eclipse projects to develop the severity prediction models. To deal with the imbalance bug data problem, we employed two approaches of ensemble by using two operators available in RapidMiner: Vote and Bagging. Results show that k-Nearest Neighbour (k-NN) performance is better than the Support Vector Machine (SVM) performance. Naive Bayes f-measure performance is poor, i.e. below 34.25%. In case of k-NN, developing training candidates by combining more than one training datasets helps in improving the performances (f-measure and accuracy). The two ensemble approaches have improved the f-measure performance up to 5% and 10% respectively for the severity levels having less number of bug reports in comparison of major severity level. We have further motivated the paper with a cross project bug severity prediction between Eclipse and Mozilla products. Results show that Mozilla products can be used to build reliable prediction models for Eclipse products and vice versa in case of SVM and k-NN classifiers.Article Citation Count: 18Quantitative Quality Evaluation of Software Products by Considering Summary and Comments Entropy of a Reported Bug(Mdpi, 2019) Mısra, Sanjay; Misra, Ananya; Misra, Sanjay; Fernandez Sanz, Luis; Damasevicius, Robertas; Singh, V. B.; Computer EngineeringA software bug is characterized by its attributes. Various prediction models have been developed using these attributes to enhance the quality of software products. The reporting of bugs leads to high irregular patterns. The repository size is also increasing with enormous rate, resulting in uncertainty and irregularities. These uncertainty and irregularities are termed as veracity in the context of big data. In order to quantify these irregular and uncertain patterns, the authors have appliedentropy-based measures of the terms reported in the summary and the comments submitted by the users. Both uncertainties and irregular patterns have been taken care of byentropy-based measures. In this paper, the authors considered that the bug fixing process does not only depend upon the calendar time, testing effort and testing coverage, but it also depends on the bug summary description and comments. The paper proposed bug dependency-based mathematical models by considering the summary description of bugs and comments submitted by users in terms of the entropy-based measures. The models were validated on different Eclipse project products. The models proposed in the literature have different types of growth curves. The models mainly follow exponential, S-shaped or mixtures of both types of curves. In this paper, the proposed models were compared with the modelsfollowingexponential, S-shaped and mixtures of both types of curves.