Predicting Software Defects with Explainable Machine Learning

Santos, Geanderson E. dos; Figueiredo, Eduardo; Veloso, Adriano; Viggiato, Markos; Ziviani, Nívio

doi:10.1145/3439961.3439979

Cited by 13 publications

(5 citation statements)

References 41 publications

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“…Consequently, it would assist the researchers and practitioners in reasoning about the underlying logic of an ML during the SDP process. Santos et al 96 used an XGBoost algorithm on multiple NASA datasets with AUC and a new performance measure called SHapley Addictive exPlanation (SHAP). Transparency, interpretability, and explainability all play significant roles.…”

Section: Resultsmentioning

confidence: 99%

A research landscape on software defect prediction

Taskeen

Khan

Felix

2023

J Software Evolu Process

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Software defect prediction is the process of identifying defective files and modules that need rigorous testing. In the literature, several secondary studies including systematic reviews, mapping studies, and review studies have been reported. However, no research work such as a tertiary study that combines secondary studies has focused on providing a landscape of software defect prediction useful to understand the body of knowledge. Motivated by this, we intend to perform a tertiary study by following a systematic literature review protocol to provide a research landscape of the targeted domain. We synthesize the quality of the secondary studies and investigate the employed techniques and the performance evaluation measures for evaluating the software defect prediction model. Furthermore, this study aims at exploring different datasets employed in the reported experimentation. Moreover, the current study intends at highlighting the research trends, gaps, and opportunities in the targeted research domain. The results indicate that none of the reported defect prediction techniques can be regarded as the best; however, the reported techniques performed better in different testing situations. In addition, machine learning (ML)‐based techniques perform better than traditional statistical techniques mainly due to the potential of discovering the defects and generating generalized results. Moreover, the obtained results highlight the need for further work in the domain of ML‐based techniques. Furthermore, publicly available datasets should be considered for experimentation or replication purposes. The potential future work can focus on data quality, ethical ML, cross‐project defect prediction, early defect prediction process, class imbalance problem, and model overfitting.

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Section: Resultsmentioning

confidence: 99%

A research landscape on software defect prediction

Taskeen

Khan

Felix

2023

J Software Evolu Process

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“…The results and discussion will analyze the model's performance and interpretability, offering insights into the applicability and impact of the proposed approach. Finally, the paper will conclude with a discussion on the implications of these findings for the field of software engineering and propose directions for future research to further refine and expand upon the work presented here [4,5].…”

Section: Introductionmentioning

confidence: 84%

Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability

Ali,

Sabir

2024

Emerg. Technol. Eng. J.

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This paper introduces a novel hybrid machine learning model that combines Long Short-Term Memory (LSTM) networks and SHapley Additive exPlanations (SHAP) to enhance bug localization across multiple software platforms. The aim is to adapt to the variability inherent in different operating systems and provide transparent, interpretable results for software developers. Our methodology includes comprehensive preprocessing of bug report data using advanced natural language processing techniques, followed by feature extraction through word embeddings to accommodate the sequential nature of text data. The LSTM model is trained and evaluated on a dataset of simulated bug reports, with the results interpreted using SHAP values to ensure clarity in decision-making. The results demonstrate the model’s robustness, adaptability, and consistent performance across platforms, as evidenced by accuracy, precision, recall, and F1 scores. The dataset's distribution of bug categories and statuses further provides valuable insights into common software development issues.

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“…Nevertheless, they neglected to address the bugs responsible for their occurrence. [24] The researchers investigated SDP models by using a very efficient version of the XGBoost algorithm. This endeavor yielded millions of randomized models.…”

Section: Proposed Approachmentioning

confidence: 99%

LCNN: Lightweight CNN Architecture for Software Defect Feature Identification Using Explainable AI

Begum,

Hasan Shuvo,

Kamal Nasir

et al. 2024

IEEE Access

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Predicting Software Defects with Explainable Machine Learning

Cited by 13 publications

References 41 publications

A research landscape on software defect prediction

A research landscape on software defect prediction

Cross-Platform Bug Localization Strategies: Utilizing Machine Learning for Diverse Software Environment Adaptability

LCNN: Lightweight CNN Architecture for Software Defect Feature Identification Using Explainable AI

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