A Survey - Software Fault Prediction Techniques

Main Article Content

R. Sathyaraj

Abstract

Software engineering is the profession to analysis, design, development and maintenance of software. In the development of software, quality is the main constraint about the success of the software design. To measure and improve the quality of software different prediction approaches are available like test effort prediction, correction cost prediction, software fault prediction (SFP), security prediction and so on. This paper describes predicting the software fault from the previous outputs and methodologies. SFP is the best approach to predict the quality of software and the review taken between the year 1998 and 2010 and also discussed with the atasets, methodologies, evaluation of performance using various metrics. This paper gives the overview about the prediction of software fault which was discussed so far and trends which are currently used.

Downloads

Download data is not yet available.

Article Details

Section
Articles

References

Cagatay Catal., “Software fault prediction: A literature review and current trendsâ€, Expert systems with applications Volume 38, Issue 4, April 2011, Pages 4626-4636 Elsevier.

Evett.M., Khoshgoftar.T, Chien.P and Edward Allen., “GP-based software quality predictionâ€, in 3rd annual conference on genetic programming PP. 60–65, 1998.

Khoshgoftaar. T. M, Allen. E. B and Busboom. J. C., “Modeling software quality: The software measurement analysis and reliability toolkitâ€, In 12th IEEE international conference on tools with artificial intelligence†Vancouver, BC, Canada: IEEE Computer Society, PP.54–61, 2000.

Khoshgoftaar. T, Gao.K and Szabo.R.M, “An application of zero-inflated poisson regression for software fault predictionâ€, In 12th international symposium on software reliability engineering, Washington, DC: IEEE Computer Society, PP 66–73, 2001.

Menzies.T, & Di Stefano.J.S., “How good is your blind spot sampling policy?â€, in 8th IEEE international symposium on high-assurance systems engineering Tampa, FL, USA: IEEE Computer Society, PP. 129–138, 2004

Kanmani.S, Sankaranarayanan.V, Uthariaraj.V.R, and Thambidurai.P, “Object oriented software quality predictionusing general regression neural networksâ€, ACM SIGSOFT Software Engineering Notes, Volume 29 Number 5, September 2004.

Marcus.A, Poshyvanyk.D, and Ferenc.R., “Using the conceptual cohesion of classes for fault prediction in object-oriented systemsâ€, IEEE Transactions on software engineering, vol. 34, no. 2, March/April 2008.