By Topic

Software Release Time Management: How to Use Reliability Growth Models to Make Better Decisions

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Chu-ti Lin ; Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan ; Chin-yu Huang

In late years, due to the significance of software application, professional testing of software becomes an increasingly important task. Once all detected faults are removed, project managers can begin to determine when to stop testing. Software reliability has important relations with many aspects of software, including the structure, the operational environment, and the amount of testing. Actually, software reliability analysis is a key factor of software quality and can be used for planning and controlling the testing resources during development. Over the past three decades, many software reliability growth models (SRGMs) have been proposed. For most traditional SRGMs, one common assumption is that the fault detection rate is a constant over time. However, the fault detection process in the operational phase is different from that in the testing phase. Thus, in this paper, we use the testing compression factor (TCF) to reflect the fact and describe the possible phenomenon. In addition, sometimes the one-to-one mapping relationship between failures and faults may not be realistic. Therefore, we also incorporate the concept of quantified ratio, not equal to 1, of faults to failures into software reliability growth modeling. We estimate the parameters of the proposed model based on real software failure data set and give a fair comparison with other SRGMs. Finally, we show how to use the proposed model to conduct software release time management

Published in:

2006 IEEE International Conference on Management of Innovation and Technology  (Volume:2 )

Date of Conference:

21-23 June 2006