TOWARDS AUTOMATED REGRESSION ANALYSIS AND MANAGEMENT THROUGHOUT SOFTWARE LIFE-CYCLE

##plugins.themes.academic_pro.article.sidebar##

Published Nov 1, 2015
https://doi.org/10.47164/ijngc.v6i3.400
Download
PDF
Statistic

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...
Volume 6, Issue 3, November 2015

##plugins.themes.academic_pro.article.main##

Ankur Gupta
Veena Tripathi

Abstract

Software application performance tends to degrade over a period of time due to introduction of new features, feature enhancements and bugs-fixes as part of the software maintenance lifecycle. Critical applications cannot afford the performance degradation. However, teams engaged in software maintenance are not too effective in detecting potential performance impacting issues during the test and release phases of the software engineering process, since the run-time environments for individual customers are difficult to simulate. This challenge is exacerbated for applications with a large installed-base. This research paper proposes the concept of Application Baselining as a means to effectively detecting and containing software regression. It provides indication of real-time application performance by monitoring its critical parameters over long periods of time. By keeping track of the changes made to the application and its environment, their impact on application performance is correlated. The changes which adversely impact the application performance are then rolled-back to mitigate their effect. Early work towards development of such a framework is presented.

##plugins.themes.academic_pro.article.details##

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite
Gupta, A., & Tripathi, V. (2015). TOWARDS AUTOMATED REGRESSION ANALYSIS AND MANAGEMENT THROUGHOUT SOFTWARE LIFE-CYCLE. International Journal of Next-Generation Computing, 6(3), 206–216. https://doi.org/10.47164/ijngc.v6i3.400
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Cito, J., Suljoti, D., Leitner, P., and Dustdar, S. 2014. Identifying root causes of web performance degradation using changepoint analysis. Series Lecture Notes in Computer Science Vol.8541, pp.181{199. DOI: https://doi.org/10.1007/978-3-319-08245-5_11
  2. Gupta, A. 2012. Practitioner-oriented collaborative and cooperative software maintenance. In-ternational Journal of Computer Science: Theory, Technology and Applications Vol.1, No.1.
  3. Gupta, A. and Shamim, S. Method of regression analysis for software maintenance throughout its lifecycle. Patent Application 3359/DEL/2013.
  4. Jin, G., Song, L., Shi, X., Scherpelz, J., and Lu, S. 2012. Understanding and detecting real- world performance bugs. In 33rd ACM SIGPLAN Conference on Programming Language Design and Implementation. pp.77{88. DOI: https://doi.org/10.1145/2254064.2254075
  5. Lane, H. 2000. Technology regression and verication acceptance method. US Patent No:6,269,457.
  6. McKellar, B. 1996. Network baselining, part i: Understanding the past to predict the future.WG Sales.
  7. Walker, J. L., Diab, S., and Slovik, A. 1998. Method for dening durable data for regression testing. US Patent No: 6,061,643.
  8. Zheng, P., Qi, Y., Zhou, Y., Chen, P., Zhan, J., and Lyu, M. 2014. An automatic framework for detecting and characterizing performance degradation of software systems.IEEE Transactions on Reliability Vol.63, No.4. DOI: https://doi.org/10.1109/TR.2014.2338255