Using error abstraction and classification to improve requirement quality: conclusions from a family of four empirical studies

  • Authors:

  • Gursimran S. Walia;Jeffrey C. Carver

  • Affiliations:

  • Department of Computer Science, North Dakota State University, Fargo, USA;Department of Computer Science, University of Alabama, Tuscaloosa, USA

  • Venue:
  • Empirical Software Engineering
  • Year:
  • 2013

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Abstract

Achieving high software quality is a primary concern for software development organizations. Researchers have developed many quality improvement methods that help developers detect faults early in the lifecycle. To address some of the limitations of fault-based quality improvement approaches, this paper describes an approach based on errors (i.e. the sources of the faults). This research extends Lanubile et al.'s, error abstraction process by providing a formal requirement error taxonomy to help developers identify both faults and errors. The taxonomy was derived from the software engineering and psychology literature. The error abstraction and classification process and the requirement error taxonomy are validated using a family of four empirical studies. The main conclusions derived from the four studies are: (1) the error abstraction and classification process is an effective approach for identifying faults; (2) the requirement error taxonomy is useful addition to the error abstraction process; and (3) deriving requirement errors from cognitive psychology research is useful.