Integrating Software Engineering and Human Error Models
To Improve Software Quality

NSF Award Abstract (CCF-1421006)

This research looks to improve software quality in a new way by assuming that human error is a key cause of software defects. Research from cognitive psychology is used to develop a deeper understanding of the human errors that occur during the software development process and to develop techniques that detect and prevent those errors early in the software development lifecycle. Early elimination of mistakes will improve software quality and reduce overall development cost. Through the application of human error research from psychology, this work will improve developers? ability to identify, classify, and eliminate software development errors and provide a solid structure and theoretical basis upon which to build.

To effectively use findings from human error research to improve software quality, this project has three primary objectives. The first objective is to develop and empirically validate a requirement error taxonomy. The inclusion of human error research will ensure that the taxonomy is well-structured. Empirical evaluation with developers will ensure the taxonomy is complete and useful. The second objective is to use the taxonomy to build and empirically evaluate error-based software development techniques. These detection and prevention techniques will operationalize the error information into a format usable by developers. The third objective is to develop tool support for the error-based techniques. The use of human error research in this project will provide a more in-depth understanding of the types of mistakes developers make during development. In addition to its impacts on software quality, this project will also provide a venue for software engineering researchers to interact with cognitive psychologists, producing more diverse PhD students.

Please visit the workshop page for links to workshops related to this project.

For more information contact Jeffrey Carver or Gursimran Walia.
Last Updated on December 4, 2014 by Jeffrey Carver
This material is based upon work supported by the National Science Foundation under Grant No. CCF-1421006. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.