Can We Predict the Change in Code in a Software Product Line Project?

Alshehri, Yasser

doi:10.4236/jsea.2020.136007

Cited by 3 publications

(5 citation statements)

References 25 publications

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“…We combined g-lasso with SMOTE to treat unbalanced data and improve prediction. Predicting change in software unit was also explored in our recent works [4,5] using popular classifiers (i.e., logistic regression, decision tree, and random forest).…”

Section: Related Workmentioning

confidence: 99%

“…Quality data means that internal validity is not violated. This research's data was extracted by early works [14,58,59] and used in other research [2,4,20]. The confidence in the data is very high.…”

Section: Threats To Validitymentioning

confidence: 99%

“…In previous works [2,4,5], the focus on prediction was adding more predictors, trying different algorithms, or using techniques to improve prediction. This work analyzes predictors to determine what predictor can be used as confounding and split the dataset.…”

Section: Introductionmentioning

confidence: 99%

“…Lines of code is the most common static code metric, defining the entire code lines on a software unit. Lines of code with other static code metrics are usually used as predictors for software faults ( [2,7]) or predicting software files change (e.g., [4,5]). In this study, we use the size metric to split or stratify the dataset into multiple groups rather than as a predictor.…”

Section: Introductionmentioning

confidence: 99%

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Explore the effect of size stratification of datasets on software faults proneness prediction performance

Alshehri,

Alshehri

2023

Preprint

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Size is one of the significant factors associated with bugs, and it has been used to predict software faults. We believe that stratifying software files based on size can play an essential role in improving prediction performance. This study explores the effect of size by stratifying our sample based on each unit’s size and distributing software units in multiple stratified groups based on an equal distribution approach. We stratified the Eclipse Europa project and Derby project files, and we reported the performance of each stratified group and compared them. We used two popular classifiers, decision tree J48 and random forest, to implement this experiment. These classifiers presented similar results on the same group of files. The results indicated that predicting faults with large files is significantly better than predicting those with small files. Furthermore, the files of large groups provide reliable and more stable performance. Finally, we resampled all the stratified groups of the two projects to explore if this can impact the performance. We found that the resampling has no impact on the performance of algorithms using the Europa dataset. However, the effect was significant when the Derby project was used.

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Section: Related Workmentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Explore the effect of size stratification of datasets on software faults proneness prediction performance

Alshehri,

Alshehri

2023

Preprint

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“…In Reference 30, the author explored how to predict change on a software product line project. Also, the study examined how different learners perform.…”

Section: Related Workmentioning

confidence: 99%

Predicting change in newly created files in a software product line project

Alshehri

2020

Softw Pract Exp

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At the beginning of the testing phase and before the deployment phase of a project's development cycle, we need to predict files with a high chance of change. Software products are always prone to change due to several reasons, including fixing errors or improvements. In this work, we used the Eclipse (releases from 2.0 to 3.5) to investigate how prediction models can perform when learning from a release and predicting in the subsequent one, which contains new files that models have not seen. We compared the performance of these models with models that are trained and tested on the same release. We found no differences between predicting the same release or subsequent release on two pre Europa releases. Predicting change in newly created files helps improve maintenance planning for software project managers and reduce cost. It will also help to enhance the quality of software by improving the practices of developers.This study used the Adaptive Boost classifier with the decision tree J48 algorithm and combined it with the re-sampling method. We find this to be better than using a meta classifier alone or combine the re-sampling with the standard classification. We compared our results with related works and found that our results are outperforming.

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