Context: Quality assurance effort, especially testing effort, is frequently a major cost factor during software development. Consequently, one major goal is often to reduce testing effort. One promising way to improve the effectiveness and efficiency of software quality assurance is the use of data from early defect detection activities to provide a software testing focus. Studies indicate that using a combination of early defect data and other product data to focus testing activities outperforms the use of other product data only. One of the key challenges is that the use of data from early defect detection activities (such as inspections) to focus testing requires a thorough understanding of the relationships between these early defect detection activities and testing. An aggravating factor is that these relationships are highly context-specific and need to be evaluated for concrete environments. Objective: The underlying goal of this paper is to help companies get a better understanding of these relationships for their own environment, and to provide them with a methodology for finding relationships in their own environments. Method: This article compares three different strategies for evaluating assumed relationships between inspections and testing. We compare a confidence counter, different quality classes, and the F-measure including precision and recall. Results: One result of this case-study-based comparison is that evaluations based on the aggregated F-measures are more suitable for industry environments than evaluations based on a confidence counter. Moreover, they provide more detailed insights about the validity of the relationships. Conclusion: We have confirmed that inspection results are suitable data for controlling testing activities. Evaluated knowledge about relationships between inspections and testing can be used in the integrated inspection and testing approach In2Test to focus testing activities. Product data can be used in addition. However, the assumptions have to be evaluated in each new context
Abstract-Product metrics, such as size or complexity, are often used to identify defect-prone parts or to focus quality assurance activities. In contrast, quality information that is available early, such as information provided by inspections, is usually not used. Currently, only little experience is documented in the literature on whether data from early defect detection activities can support the identification of defectprone parts later in the development process. This article compares 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. These 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.
! 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 (e.g., size and complexity metrics, inspection defect content metrics, and defect density metrics). 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, which substantiates the expected benefit of using inspection results to optimize testing.
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