Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

Matloob, Faseeha; Aftab, Shabib; Ahmad, Munir; Khan, Muhammad Adnan; Fatima, Areej; Iqbal, Muhammad; Alruwaili, Wesam Mohsen; Elmitwally, Nouh Sabri

doi:10.32604/iasc.2021.017562

Cited by 12 publications

(24 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Neural networks from the same category are also regarded as one of the most promising techniques for defect prediction models. 46,54,78,79 Wahono 43 concludes that there is difficulty in choosing optimal network architectural characteristics such as the number of hidden neurons, learning rate, momentum, and training cycles, which limits the applicability of NN. No single classifier works well across all datasets.…”

Section: Rq31: What Type Of Techniques/methods Have Been Proposed In ...mentioning

confidence: 99%

“…As precision and recall are trade-offs, the F-measure has been employed in a number of studies. 42,48,53,54,78,88,[90][91][92] 2…”

Section: Rq4: What Different Performance Measures Have Been Proposed ...mentioning

confidence: 99%

“…To predict faults with more accuracy and precision, researchers have suggested hybrid, meta-learning, and network-based frameworks. 54 Despite the failure, the researchers have aimed to develop a universal defect model and cope with diverse data. Future research should look at developing a highly accurate and scalable (pre-trained) defect analytical model with the lowest implementation cost while maintaining explainability.…”

Section: Rq4: What Different Performance Measures Have Been Proposed ...mentioning

confidence: 99%

See 2 more Smart Citations

A research landscape on software defect prediction

Taskeen

Khan

Felix

2023

J Software Evolu Process

View full text Add to dashboard Cite

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.

show abstract

Section: Rq31: What Type Of Techniques/methods Have Been Proposed In ...mentioning

confidence: 99%

“…As precision and recall are trade-offs, the F-measure has been employed in a number of studies. 42,48,53,54,78,88,[90][91][92] 2…”

Section: Rq4: What Different Performance Measures Have Been Proposed ...mentioning

confidence: 99%

Section: Rq4: What Different Performance Measures Have Been Proposed ...mentioning

confidence: 99%

See 1 more Smart Citation

A research landscape on software defect prediction

Taskeen

Khan

Felix

2023

J Software Evolu Process

View full text Add to dashboard Cite

show abstract

“…Many kinds of models have been used to tackle SDP. Researchers have proposed using supervised models [63], [64], [65], semi-supervised models [66], [67], unsupervised models [23], tasks specific models such as BugCache [22] and even approaching the problem as an anomaly detection problem [20], [21].…”

Section: B Metrics Prediction Granularity and Approaches To Sdpmentioning

confidence: 99%

Empirical Study: How Issue Classification Influences Software Defect Prediction

et al. 2023

View full text Add to dashboard Cite

Software defect prediction aims to identify potentially defective software modules to better allocate limited quality assurance resources. Practitioners often do this by utilizing supervised models trained using historical data. This data is gathered by mining version control and issue tracking systems. Version control commits are linked to issues they address. If the linked issue is classified as a bug report, the change is considered as bug fixing. The problem arises from the fact that issues are often incorrectly classified within issue tracking systems. This introduces noise into the gathered datasets. In this paper, we investigate the influence issue classification has on software defect prediction dataset quality and resulting model performance. To do this, we mine data from 7 popular open-source repositories, create issue classification and software defect prediction datasets for each of them. We investigate issue classification using four different methods; a simple keyword heuristic, an improved keyword heuristic, the FastText model and the RoBERTa model. Our results show that using the RoBERTa model for issue classification produces the best software defect prediction datasets, containing on average 14.3641% of mislabeled instances. SDP models trained on such datasets achieve superior performance, to those trained on SDP datasets created using other issue classification methods, in 65 out of 84 experiments, with 55 of them being statistically relevant. Furthermore, in 17 out of 28 experiments we could not show a statistically relevant performance difference between SDP models trained on RoBERTa derived software defect prediction datasets and those created using manually labeled issues.

show abstract

“…To select the highquality research papers, a systematic research process is followed as explained by [34,35]. Moreover, the detailed guidelines are also taken from [36][37][38][39][40][41][42]. e systematic research process followed by this study consists of 9 steps as shown in Figure 1.…”

Section: Research Protocolmentioning

confidence: 99%

Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review

Khan

Elmitwally

Abbas

et al. 2022

Scientific Programming

Self Cite

View full text Add to dashboard Cite

The demand for automated online software systems is increasing day by day, which triggered the need for high-quality and maintainable softwares at lower cost. Software defect prediction is one of the crucial tasks of the quality assurance process which improves the quality at lower cost by reducing the overall testing and maintenance efforts. Early detection of defects in the software development life cycle (SDLC) leads to the early corrections and ultimately timely delivery of maintainable software, which satisfies the customer and makes him confident towards the development team. In the last decade, many machine learning-based approaches for software defect prediction have been proposed to achieve the higher accuracy. Artificial Neural Network (ANN) is considered as one of the widely used machine learning techniques, which is included in most of the proposed defect prediction frameworks and models. This research provides a critical analysis of the latest literature, published from year 2015 to 2018 on the use of Artificial Neural Networks for software defect prediction. In this study, a systematic research process is followed to extract the literature from three widely used digital libraries including IEEE, Elsevier, and Springer, and then after following a thorough process, 8 most relevant research publications are selected for critical review. This study will serve the researchers by exploring the current trends in software defect prediction with the focus on ANNs and will also provide a baseline for future innovations, comparisons, and reviews.

show abstract

Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review

Cited by 12 publications

References 31 publications

A research landscape on software defect prediction

A research landscape on software defect prediction

Empirical Study: How Issue Classification Influences Software Defect Prediction

Software Defect Prediction Using Artificial Neural Networks: A Systematic Literature Review

Contact Info

Product

Resources

About