Software Defect Prediction Based on Fourier Learning

Yang, Kang; Yu, Huiqun; Fan, Guisheng; Yang, Xingguang; Zheng, Shuang; Leng, Chunxia

doi:10.1109/pic.2018.8706304

Cited by 2 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [17] , In order to detect software defects, you need develop techniques to calculate Fourier coefficients and obtain the expected function. Also, we evaluate Fourier learning against standard ML techniques, like the RF method.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Developed Supervised Machine Learning System For Classification And Prediction of Software Faults Using NASA Dataset

Gupta,

Sinha

2023

IJRITCC

View full text Add to dashboard Cite

The software systems of modern computers are extremely complex and versatile. Therefore, it is essential to regularly detect and correct software design faults. In order to devote resources effectively towards the creation of trustworthy software, software companies are increasingly engaging in the practise of predicting fault-prone modules in advance of testing. These software fault prediction methods rely on the thoroughness with which prior software versions' fault as well as related code has been retrievedTime, energy, and money are all saved as a result. Increases the company's initial success and bottom line greatly by satisfying its clientele. Numerous academics have poured into this area throughout the years in an effort to raise the bar for all software. Nowadays, The most often used approaches in this field are those based on machine learning (ML). The field of ML seeks to perfect software capable of evolving as well as adapting in response to fresh data. This paper introduces a fresh approach for doing ML by bringing together a number of different expert systems. In order to reach agreement on which aspects of a software system need to be tested, the proposed multi-classifier model pools the strengths of the most effective classifiers. Several top-performing classifiers for defect prediction are put through their paces in an experiential evaluation. We test our method on 16 publicly available datasets from the NASA Metric Data Programme (MDP) repository at the promise repository. Parameters of confusion, recall, precision, recognition accuracy, etc., are evaluated and contrasted with existing schemes in a software analysis performed with the help of the python simulation tool with findings. The experimental outcomes demonstrate that by combining LGBM, XGBoost, and Voting classifiers, using a multi classifier approach, we are capable to significantly improve software fault prediction performance. The results of the investigation show that the suggested method will lead to better practical outcomes in the prediction of device failures.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Ignore many faults in favor of the simpler challenge of categorizing faults into two groups. [17] Fourier coefficient and Fourier learning algorithm…”

Section: Promise Repository Datasetmentioning

confidence: 99%