Tertiary Study on Landscaping the Review in Code Smells

Yaqoob, R.; Sanaa,; Khan, Saif Ur Rehman; Shah, M. A.

doi:10.1049/icp.2021.2421

Cited by 1 publication

(1 citation statement)

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“…Software defects commonly arise during an SDLC process; however, the prediction of defects remains a challenging task. This is because software defects may lead to complexity and system failure and increase the software system's maintainability costs 2 . The core purpose of SDP is to predict the possibility of a file or commit being defective.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%