Prospects and Challenges of using Machine Learning Algorithms for Software Quality Assessment and Prediction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Ananthi Sheshasaayee, Roby Jose

Ananthi Sheshasaayee and Roby Jose. Article: Prospects and Challenges of using Machine Learning Algorithms for Software Quality Assessment and Prediction. International Journal of Computer Applications 129(7):33-35, November 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Ananthi Sheshasaayee and Roby Jose},
	title = {Article: Prospects and Challenges of using Machine Learning Algorithms for Software Quality Assessment and Prediction},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {129},
	number = {7},
	pages = {33-35},
	month = {November},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Maintainability of the software is one of the key quality while evaluating software product. Of the overall software development cost, major stake is employed at the maintenance phase. Maintenance time of software is always greater than its development time, so it becomes essential to measure the maintainability of software so that maintenance operational time can be brought down.While going over the prevailing knowledge of literature it is understood that maintenance cost can be regulated by using software metrics at the design phase. There is substantial works in proving that machine learning algorithms is a suitable alternative for many domains of computational sciences including software engineering. This paper is aimed at carrying out a detailed study on the usage of machine learning approaches in the prediction, assessment and evaluation of software maintainability


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Machine learning algorithms, Maintainability Prediction, Software Maintainability Prediction Models and Metrics, Software metrics.