Machine Learning for Software Defect Prediction

Predicting which software components are more likely to have defects might be useful. Many empirical assessments have thus been conducted to assess the effectiveness of various strategies aimed at achieving this. Designing a trustworthy defect prediction model is still difficult, as no single method is superior. Systems requiring rapid modification based on actual mission-specific objectives ought to be subject to computerised reliability evaluation. Software defect prediction techniques based on neural networks can be employed to monitor and evaluate the system as one step in this process. The aforementioned objectives call for the use of instance-based learning methods because of the ever-evolving character of the software data that is gathered. Generally speaking, the steps of evaluation, creation, execution, testing, and release comprise the software development life cycle. To ensure that end users receive software that is free of bugs, the testing step must be run efficiently. A number of machine learning approaches have been used for more reliable prognosis as academics' enthusiasm for the software defect detection problem has grown.

The increasing complexity of software systems and client demands led to the introduction of a standard of quality assurance in software and the assessment of software quality. This standard states that measurements, within as well as outside, are used to determine a software product's quality attributes. The assumption used by the majority of machine learning techniques is that each category's misperception expense has equal importance. Generally speaking, nevertheless, a minority class's categorization cost is greater than overwhelming. A percentage of countable or measurable attributes that could be used to gauge and forecast software quality is characterised as an application metric. One of the most important and costly stages of the lifecycle of software creation is the prognosis of software defects, which is crucial to improving the quality of software systems according to the software development philosophy. As software systems become more prevalent in  everyday lives, their interdependence and complexities grow as well, creating an atmosphere that is conducive to flaws. It can be challenging to predict software's defect-prone areas until significant effort is put into finding flaws.

Software flaws and defects must be eliminated for software to be reliable and high-quality. Software code is the primary cause of problems in software; however, some can arise from non-code reasons. Testing and reviewing software is the conventional method of identifying errors in it. But it's possible that these tasks will take a lot of effort and time to complete. The software defect prediction that emerges with regard to the implications and difficulties that the digital society offers both for and against the economy is covered in this special issue collection.

We welcome articles exploring topics including, but not limited to:

1.    Empirical evaluation of software defect prediction methods based on machine learning

2.    Predicting software defects with ensemble methods and trained machine learning

3.    An enhanced software fault prediction method with machine learning

4.    An analogous of software fault prediction techniques based on machine learning

5.    Regarding the prediction of software defects using machine learning

6.    An extensive analysis of machine learning  for predicting software defects

7.    Industrial usage of machine learning for software defect forecasting

8.    An overview of machine learning approaches for predicting the software defects

9.    An empirical evaluation of machine learning methods for predicting software defects

10. State of the art for predicting software defects with machine learning techniques

11. Appraisal of machine learning methods' effectiveness in predicting software defects

The Guest Editors for this SI are,

Dr. Sobia Wassan

School of Equipment Engineering,

Jiangsu Urban and Rural Construction Vocational College,

Changzhou 213000, China.

Email: sobiaali614@gmail.com, sobiawassan8@gmail.com

Google Scholar: https://scholar.google.com/citations?user=wDQwkrwAAAAJ&hl=en

Dr. Danish Vasan

King Fahad University of Petroleum and Minerals,

Saudi Arabia.

Email: danish.vasan@kfupm.edu.sa

Google Scholar: https://scholar.google.com/citations?user=Xh3BvTgAAAAJ&hl=en

Dr. Beenish Suhail

School of Economics,

Shanghai University,

Shanghai 201900, China

Email: binish.wasan@gmail.com

Researchgate: https://www.researchgate.net/profile/Beenish-Suhail

Dr.Shao yu Zhang

Jiangsu Urban and Rural Construction College,

Changzhou 213147, China

Email: zsy_682206@163.com

Google Scholar: https://scholar.google.com/citations?user=p3ePL4QAAAAJ&hl=en

The timeline for this SI will be as follows,

Submission Deadline           -        20.01.2025

Authors Notification            -         25.03.2024

Final notification                 -        26.05.2025