DocumentCode :
3212931
Title :
Statistical analysis of time series data on the number of faults detected by software testing
Author :
Amasaki, Sousuke ; Yoshitomi, Takashi ; Mizuno, Osamu ; Kikuno, Tohru ; Takagi, Yasunari
Author_Institution :
Graduate Sch. of Inf. Sci. & Technol., Osaka Univ., Japan
fYear :
2002
fDate :
18-20 Nov. 2002
Firstpage :
272
Lastpage :
277
Abstract :
According to a progress of the software process improvement, the time series data on the number of faults detected by the software testing are collected extensively. In this paper, we perform statistical analyses of relationships between the time series data and the field quality of software products. At first, we apply the rank correlation coefficient τ to the time series data collected from actual software testing in a certain company, and classify these data into four types of trends: strict increasing, almost increasing, almost decreasing, and strict decreasing. We then investigate, for each type of trend, the field quality of software products developed by the corresponding software projects. As a result of statistical analyses, we showed that software projects having trend of almost or strict decreasing in the number of faults detected by the software testing could produce the software products with high quality.
Keywords :
program testing; software maintenance; software process improvement; software quality; statistical analysis; time series; almost decreasing; almost increasing; field quality; rank correlation coefficient; software projects; software testing; statistical analysis; strict decreasing; strict increasing; time series data; Costs; Fault detection; Information science; Performance evaluation; Software maintenance; Software performance; Software quality; Software testing; Statistical analysis; Time series analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Test Symposium, 2002. (ATS '02). Proceedings of the 11th Asian
ISSN :
1081-7735
Print_ISBN :
0-7695-1825-7
Type :
conf
DOI :
10.1109/ATS.2002.1181723
Filename :
1181723
Link To Document :
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