A semi-automated filtering technique for software process tailoring using neural network

  • Authors:

  • Soojin Park;Hoyoung Na;Sooyong Park;Vijayan Sugumaran

  • Affiliations:

  • Software Engineering Laboratory, Department of Computer Science, Sogang University, 1 Shinsoo-Dong, Mapo-Ku, Seoul 121-742, South Korea;Software Engineering Laboratory, Department of Computer Science, Sogang University, 1 Shinsoo-Dong, Mapo-Ku, Seoul 121-742, South Korea;Software Engineering Laboratory, Department of Computer Science, Sogang University, 1 Shinsoo-Dong, Mapo-Ku, Seoul 121-742, South Korea;Department of Decision and Information Sciences, School of Business Administration, Oakland University, Rochester, MI 48309, USA

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2006

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Abstract

It is widely known that implementation of the software development process to fit a given environment is the key to develop software at the lowest cost and highest quality. In general, applying an off-the-shelf software development process or an organizational process to a specific project can cause a lot of overhead if no effort is made to customize the given generic processes. Even though the process tailoring activities are done before starting a project, they are not given high importance. These activities depend on several process engineers who have a lot of experience and knowledge about process tailoring. Because of this dependence on human experience, it takes a long time to have a tailored process fit the project. To decide whether a specific task should be part of a given project or not is very time-consuming. Therefore, we suggest a semi-automated process tailoring method, which uses the artificial-neural network-based learning theory to reduce this time. We have demonstrated the effectiveness of our process filtering technique with a case study using process tailoring historical data as learning data.