Open Issues in Software Defect Prediction

Arora, Ishani; Tetarwal, Vivek; Saha, Anju

doi:10.1016/j.procs.2015.02.161

Cited by 56 publications

(21 citation statements)

References 24 publications

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“…Nevertheless, the challenge of unavailability of the local data faces the organization's team. The reasons for unavailability may be due to the changing of technology or no similar features of projects previously developed [1]. To overcome this problem, the cross-project software defect prediction is used.…”

Section: Literature Surveymentioning

confidence: 99%

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Cross-projects software defect prediction using spotted hyena optimizer algorithm

2020

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Cross-projects software defect prediction improves the quality of new software projects or projects with a shortage of historical data. Therefore, various data mining techniques are recommended in this field. The classification accuracy issue is considered one of the most significant problems due to the shortage and heterogeneous in historical data. To address this challenge, this research utilizes a spotted hyena optimizer algorithm as a classifier to predict defects through cross-projects. Confidence and Support are utilized as a multi-objective fitness function to look for the best classification rules. These classification rules are used to predict defects for new projects or other projects with insufficient data. The datasets of NASA such as JM1, KC1, and KC2 are used. By applying spotted hyena optimizer algorithm as a classifier on one dataset and predicting defects in the other two datasets, accuracy is reported 84.6, 92.0, 82.4, 90.7, 86.6 and 81.8 for JM1, KC1, and KC2 respectively. These accuracy values are better than the most significant data mining techniques in the field such as Support Vector Machine, Naïve Bayes, Boosting, C4.5, and Bagging. Also, the proposed research discusses other performance measures such as precision, recall, and f-measure. The conclusion proves that there are many features of McCabe and Halstead that have a strong impact to generate highly accurate predictors for defects such as McCabe's line count of code, McCabe's cyclomatic complexity, McCabe's essential complexity, McCabe's design complexity iv, Halstead's effort, Halstead's time estimator, Halstead's line count, Halstead's count of line of comments and total operators.

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Section: Literature Surveymentioning

confidence: 99%

“…Nowadays, Software Defect Prediction (SDP) is very critical in software engineering and one of the most helping activities during the testing phase of the System Development Life Cycle (SDLC). However, predicting the defective modules isn't a straight forward job [1].…”

Section: Introductionmentioning

confidence: 99%

Cross-projects software defect prediction using spotted hyena optimizer algorithm

2020

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“…For predicting software defects the authors used multivariate analysis [16], [17] and showed that the accuracy of software quality prediction was not affected by decreasing independent factors. The authors also used zero-inflated Poisson Regression In paper [14], an aggregate of problems was discussed with existing state-of-the-art defect prediction approaches. The authors identified the problems as follows: i) they found the set of attributes to be correlated with fault, ii) there is an absence of standard measures for performance assessment, iii) there are problems with cross project defect prediction, iv) there are inconsistencies with the economics of software defect prediction, v) the class imbalance problem and vi) the absence of any general framework for the software defect prediction.…”

Section: Related Workmentioning

confidence: 99%

“…This allowed a direct comparison of the different approaches proposed in literature [12], [13]. Six issues with existing state-of-the-art techniques were discussed in [14] and while there have been a few papers that try to address the issues raised in the paper, some of the core problems still remain. However, these approaches did not address imbalanced data, thus being less effective for real world problems.…”

Section: Introductionmentioning

confidence: 99%

Modeling Software Defects as Anomalies: A Case Study on Promise Repository

Neela¹,

Asif²,

Ami³

et al. 2017

JSW

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Software defect prediction is a highly studied domain in Software Engineering research due to its importance in software development. In literature, various classification methods with static code attributes have been used to predict defects. However, defected instances are very few compared to non-defected instances and as such lead to imbalanced data. Traditional machine learning techniques give poor results for such data. In this paper an anomaly detection technique for software defect prediction, is proposed which is not affected by imbalanced data. The technique incorporates both univariate and multivariate Gaussian distribution to model non-defected software module. The defected software modules are then predicted based on their deviation from the generated model. To evaluate our approach, we implemented the algorithm and tested it on the NASA datasets from the PROMISE repository. By utilizing this approach, we observed an average balance of 63.36% and 69.06% in univariate model and multivariate model respectively. Without utilizing optimization or filter, this approach yield better result than industry standard of 60%.

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“…Complexity metric selection as attributes or features in software defect detection still be an issue today. Not all of complexity metric influent the occurrence of software defect [3]. It needs a method to select the most influential complexity metrics.…”

Section: Introductionmentioning

confidence: 99%

Software defect detection based on selected complexity metrics using fuzzy association rule mining and defective module oversampling

Naufal

Kusuma

2019

2019 16th International Joint Conference on Computer Science and Software Engineering (JCSSE)

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Software defect is a major problem in software development. The cost of software development will be minimized when the software defects are detected earlier. Complexity metric is a mathematic calculation to calculate code complexity. It could be used to consider software defect detection. But, not all of complexity metrics influent on the occurrence of software defect, therefore it needs feature selection to select the most influent complexity metrics. Correlation-based Feature Selection (CFS) is used for selecting the most influent complexity metrics. This study conducted experiments on NASA Metric Data Program (MDP) datasets. NASA MDP contains software defect history logs based on several complexity metrics. But, there is an imbalanced distribution of defective and not defective modules in NASA MDP. The distribution of defective modules is less than not defective modules. It can reduce software defect detection performance. The distribution of defective module need to be reproduced. In this study, Synthetic Minority Oversampling Technique (SMOTE) is used to balance the distribution between defective and not defective modules. Software defect detection using Fuzzy Association Rule Mining (FARM) which is combined with the selection of complexity metrics using CFS and dataset balancing using SMOTE has sensitivity 85.51% and accuracy 91.63% in detecting software defective modules on NASA MDP dataset.

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Open Issues in Software Defect Prediction

Cited by 56 publications

References 24 publications

Cross-projects software defect prediction using spotted hyena optimizer algorithm

Cross-projects software defect prediction using spotted hyena optimizer algorithm

Modeling Software Defects as Anomalies: A Case Study on Promise Repository

Software defect detection based on selected complexity metrics using fuzzy association rule mining and defective module oversampling

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