Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
97 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
5 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Guiding Testing Activities by Predicting Defect-prone Parts Using Product and Inspection Metrics (1312.0713v1)

Published 3 Dec 2013 in cs.SE

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 defect-prone 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.

User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Frank Elberzhager (12 papers)
  2. Stephan Kremer (2 papers)
  3. Jürgen Münch (63 papers)
  4. Danilo Assmann (2 papers)
Citations (8)

Summary

We haven't generated a summary for this paper yet.