Focusing Testing by Using Inspection and Product Metrics

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http://hdl.handle.net/10138/40798

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Elberzhager , F , Kremer , S , Münch , J & Assmann , D 2013 , ' Focusing Testing by Using Inspection and Product Metrics ' , International Journal of Software Engineering and Knowledge Engineering , vol. 23 , no. 04 , pp. 433-462 . https://doi.org/10.1142/S0218194013400093

Title: Focusing Testing by Using Inspection and Product Metrics
Author: Elberzhager, Frank; Kremer, Stephan; Münch, Jürgen; Assmann, Danilo
Contributor: University of Helsinki, Department of Computer Science
Date: 2013
Language: eng
Number of pages: 29
Belongs to series: International Journal of Software Engineering and Knowledge Engineering
ISSN: 0218-1940
URI: http://hdl.handle.net/10138/40798
Abstract: A well-known approach for identifying defect-prone parts of software in order to focus testing is to use different kinds of product metrics such as size or complexity. Although this approach has been evaluated in many contexts, the question remains if there are further opportunities to improve test focusing. One idea is to identify other types of information that may indicate the location of defect- prone software parts. Data from software inspections, in particular, appear to be promising. This kind of data might already lead to software parts that have inherent difficulties or programming challenges, and in consequence might be defect-prone. This article first explains how inspection and product metrics can be used to focus testing activities. Second, we compare selected product and inspection metrics commonly used to predict defect-prone parts. Based on initial experience from two case studies performed in different environments, the suitability of different metrics for predicting defect-prone parts is illustrated. The studies revealed that inspection defect data seems to be a suitable predictor, and a combination of certain inspection and product metrics led to the best prioritizations in our contexts. In addition, qualitative experience is presented.
Subject: 113 Computer and information sciences
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