Literature Review of Software Engineering Fault prediction

Authors

  • Mustafa Zaki Mohammed Department of Software, College of Computer and Mathematics, University of Mosul, Iraq
  • Ibrahim Ahmed Saleh Department of Software, College of Computer and Mathematics, University of Mosul, Iraq

Keywords:

Artificial Intelligence, Software Engineering, Test case optimization, Fault Detection, Error Prediction

Abstract

During the software development life cycle, the testing phase is an important stage to verify the efficiency of the resulting product. With the increase project size and complexity, software testing becomes a time consuming and costly task and to address this issue we use Artificial Intelligence Techniques (AIT) which is a fruitful approach in the modern trend of delivering high quality software. It is effectively implemented to improve the outcomes of all phases of the Software Development Lifecycle (SDLC). This paper focuses more on artificial intelligence techniques for error handling to overcome the cost and time of testing and improve software quality. Error handling is done by detecting and predicting errors. Forecasting software errors is a fundamental activity in software development. This is because predicting and detecting errors before deploying the software achieves user satisfaction and improves the overall performance of the software. Moreover, anticipating software error early improves software adaptability to different environments and increases resource usage

References

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Published

2022-03-25

How to Cite

Mustafa Zaki Mohammed, & Ibrahim Ahmed Saleh. (2022). Literature Review of Software Engineering Fault prediction . iJournals:International Journal of Software & Hardware Research in Engineering ISSN:2347-4890, 10(3). Retrieved from https://ijournals.in/journal/index.php/ijshre/article/view/92