Enhancing change prediction models using developer-related factors

Identifying the change‐prone parts of software could help managers and developers to effectively allocate maintenance resource and time during early phases of software life cycle. Change‐proneness prediction on file level with binary classification methods makes such identification possible. As the fact that change‐prone files frequently account for a small part of all the files, the prediction performance of standard classification methods is not satisfying. In this paper, we employ imbalanced learning methods, including bagging, resampling, and especially their combination to reduce the performance decrease of standard classifiers caused by the class imbalance problem in change‐proneness prediction. Besides, we propose a boxplot‐based partition method to provide more proper change‐proneness label designation for the training data. Eight open‐source Java projects are chosen in the empirical study to validate the effectiveness of the combination methods in change‐proneness prediction. The experimental results of the empirical study show that combining bagging with resampling can significantly improve the prediction performance of only bagging or resampling. Of all the combination methods employed, combination of bagging with undersampling performs better than others. And support vector machine is more effective as a base classifier than J48 and naive Bayes.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Software change‐proneness prediction through combination of bagging and resampling methods

Zhu

Cheng

et al. 2018

An extensive evaluation of ensemble techniques for software change prediction

Catolino

Ferrucci

2019

Self Cite

Predicting the areas of the source code having a higher likelihood to change in the future represents an important activity to allow developers to plan preventive maintenance operations.For this reason, several change prediction models have been proposed. Moreover, research community demonstrated how different classifiers impact on the performance of devised models as well as classifiers tend to perform similarly even though they are able to correctly predict the change proneness of different code elements, possibly indicating the presence of some complementarity among them. In this paper, we deeper investigated whether the use of ensemble approaches, ie, machine learning techniques able to combine multiple classifiers, can improve the performances of change prediction models. Specifically, we built three change prediction models based on different predictors, ie, product-, process-metrics-, and developer-related factors, comparing the performances of four ensemble techniques (ie, Boosting, Random Forest, Bagging, and Voting) with those of standard machine learning classifiers (ie, Logistic Regression, Naive Bayes, Simple Logistic, and Multilayer Perceptron). The study was conducted on 33releases of 10 open-source systems, and the results showed how ensemble methods and in particular Random Forest provide a significant improvement of more than 10% in terms of F measure. Indeed, the statistical analyses conducted confirm the superiority of this ensemble technique. Moreover, the model built using developer-related factors performed better than the other models that exploit product and process metrics and achieves an overall median of F measure around 77%.

Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules

Chatzimparmpas

Bibi

2019

Managing the maintenance process and estimating accurately the effort and duration required for a new release is considered to be a crucial task as it affects successful software project survival and progress over time. In this study, we propose the combination of two well-known machine learning (ML) techniques, Bayesian networks (BNs), and association rules (ARs) for modeling the maintenance process by identifying the relationships among the internal and external quality metrics related to a particular project release to both the maintainability of the project and the maintenance process indicators (ie, effort and duration). We also exploit Bayesian inference, to test the effect of certain changes in internal and external project factors to the maintainability of a project. We evaluate our approach through a case study on 957 releases of five open source JavaScript applications. The results show that the maintainability of a release, the changes observed between subsequent releases, and the time required between two releases can be accurately predicted from size, complexity, and activity metrics. The proposed combined approach achieves higher accuracy when evaluated against the BN model accuracy. KEYWORDSdevelopers' activity, JavaScript, maintainability, maintenance, software quality, source code quality 1 | INTRODUCTION Software quality involves many different activities throughout the operational lifecycle of an application all targeted to the delivery of software that meets the specified requirements and satisfies the evolving needs of end-users. Quality concerns are often intertwined with software maintenance that is closely related to technical debt. 1 Software maintenance is one of the most demanding activities during the software lifecycle, consuming up to 75% of the total project resources, 2 mainly due to the many constraints related to the interdependencies of the source code artifacts of the software under maintenance. According to the IEEE 1219 3 software standards document, software maintenance is defined as the "Modification of a software product after delivery to correct faults, to improve performance or other attributes, or to adapt the product to a modified environment." The maintainability is "the ease of performing maintenance activities," while maintenance effort is subsequently defined as the "effort required to reduce or eliminate maintenance problems." Despite the importance of software maintenance and its crucial effect on the quality of a software application over time, its management and assessment is still an immature discipline in software engineering research and practice. The main problem is that software maintainability (as a single synthesized factor) and maintenance process indicators (ie, effort and number of modifications) are most of the times addressed separately without being able to test the effect of the low level internal and external metrics to both maintainability and maintenance indicators at the same time.This gap can be alleviated by modeling the relationship among t...