DocumentCode :
3077346
Title :
A Quantitative Approach to Software Maintainability Prediction
Author :
Ping, Liang
Author_Institution :
Southwest Univ. for Nat. (SWLUN), Chengdu, China
Volume :
1
fYear :
2010
fDate :
16-18 July 2010
Firstpage :
105
Lastpage :
108
Abstract :
Software maintainability is one important aspect in the evaluation of software evolution of a software product. Due to the complexity of tracking maintenance behaviors, it is difficult to accurately predict the cost and risk of maintenance after delivery of software products. In an attempt to address this issue quantitatively, software maintainability is viewed as an inevitable evolution process driven by maintenance behaviors, given a health index at the time when a software product are delivered. A Hidden Markov Model (HMM) is used to simulate the maintenance behaviors shown as their possible occurrence probabilities. And software metrics is the measurement of the quality of a software product and its measurement results of a product being delivered are combined to form the health index of the product. The health index works as a weight on the process of maintenance behavior over time. When the occurrence probabilities of maintenance behaviors reach certain number which is reckoned as the indication of the deterioration status of a software product, the product can be regarded as being obsolete. Longer the time, better the maintainability would be.
Keywords :
hidden Markov models; software maintenance; software management; software metrics; software quality; hidden Markov model; product health index; software evolution; software maintenance; software metrics; software product; software quality; Complexity theory; Evolution (biology); Hidden Markov models; Maintenance engineering; Software; Software measurement; hidden markov model; software maintainability; software metrics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology and Applications (IFITA), 2010 International Forum on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-7621-3
Electronic_ISBN :
978-1-4244-7622-0
Type :
conf
DOI :
10.1109/IFITA.2010.294
Filename :
5635174
Link To Document :
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