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

Gursimran S. Walia, Jeffrey C. Carver

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)625-658
Number of pages34
JournalEmpirical Software Engineering
Volume18
Issue number4
DOIs
StatePublished - Aug 2013
Externally publishedYes

Keywords

  • Empirical studies
  • Error abstraction
  • Software engineering
  • Software inspections
  • Software quality

ASJC Scopus subject areas

  • Software

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