Conference
Prediction of software faults using machine learning: A review.
| Τίτλος: | Prediction of software faults using machine learning: A review. |
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| Συγγραφείς: | Mehta, Ashu, Kaur, Amandeep |
| Πηγή: | AIP Conference Proceedings; 2026, Vol. 3202 Issue 1, p1-7, 7p |
| Θεματικοί όροι: | Software failures, Machine learning, Algorithms, Software reliability, Quality assurance, Defect tracking (Computer software development) |
| Περίληψη: | The IT sector and software professionals have long considered the likelihood of software failure to be a critical issue. Standard methodologies cannot locate software issues within an application without a broken module or prior knowledge of defects. The programme can considerably predict and fix software defects using machine learning techniques and an automated software fault recovery methodology. Both time and money are saved because the software runs more smoothly and has fewer bugs or problems. In order to identify the flaws in the software system, this article has explored numerous defects prediction algorithms. This study identifies a group of measures to gauge and categorise software system flaws. Further, different algorithms and methods used for SFP have been summarized along with their results and future scope. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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