Knowledge reuse for software reuse

  • Authors:

  • Frank McCarey;Mel Ó/ Cinné/ide;Nicholas Kushmerick

  • Affiliations:

  • (Correspd. E-mail: frank.mccarey@ucd.ie/ Tel.: +353-87-7949237/ Fax: +353-1-2697262) School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland;School of Computer Science and Informatics, University College Dublin, Belfield, Dublin 4, Ireland

  • Venue:
  • Web Intelligence and Agent Systems
  • Year:
  • 2008

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Abstract

Software reuse can provide significant improvements in software productivity and quality whilst reducing development costs. Expressing software reuse intentions can be difficult though. A developer may aspire to reuse a software component but experience difficulty expressing their reuse intentions in a manner that is compatible with, or understood by, the component retrieval system. Various intelligent retrieval techniques have been developed that assist a developer in locating or discovering components in an efficient manner. These solutions share a common shortcoming: the developer must be capable of anticipating all reuse opportunities and initiating the retrieval process. There is a need for a comprehensive technique that not only assists with retrievals but that can also identify reuse opportunities. This paper advocates that component-based reuse can be supported through knowledge collaboration. Often programming tasks and solutions are replicated; this characteristic of software can be exploited for the benefit of future developments. Through the mining of existing source code solutions, knowledge, relating to how components are used by developers, can be extracted. Based on a developer's current programming task, this knowledge can subsequently be filtered and used to recommend a candidate set of reusable components. This novel recommendation approach applies and extends commonly used Information Retrieval and Information Filtering techniques such as Collaborative Filtering, Content-Based Filtering, and Bayesian Clustering Models, to the software reuse domain. This recommendation technology is applied to several thousand open-source Java classes. The most effective recommendation algorithm produces recommendations of a high quality at a low cost.