Article citationsMore>>

Madureira, J.S., Barroso, A.S., do Nascimento, R.P.C. and Soares, M.S. (2017) An Experiment to Evaluate Software Development Teams by Using Object-Oriented Metrics. In: Gervasi, O., Murgante, B., Misra, S., Borruso, G., Torre, C.M., Rocha, A.M., Taniar, D., Apduhan, B.O., Stankova, E. and Cuzzocrea, A., Eds., Computational Science and Its Applications, Springer International Publishing, Cham, 128-144.

has been cited by the following article:

• TITLE: Static Analysis and Code Complexity Metrics as Early Indicators of Software Defects

  AUTHORS: Safa Omri, Pascal Montag, Carsten Sinz

  KEYWORDS: Static Analysis Tools, Complexity Metrics, Software Quality Assurance, Statistical Methods, Fault Proneness

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.11 No.4, April 11, 2018

  ABSTRACT: Software is an important part of automotive product development, and it is commonly known that software quality assurance consumes considerable effort in safety-critical embedded software development. Increasing the effectiveness and efficiency of this effort thus becomes more and more important. Identifying problematic code areas which are most likely to fail and therefore require most of the quality assurance attention is required. This article presents an exploratory study investigating whether the faults detected by static analysis tools combined with code complexity metrics can be used as software quality indicators and to build pre-release fault prediction models. The combination of code complexity metrics with static analysis fault density was used to predict the pre-release fault density with an accuracy of 78.3%. This combination was also used to separate high and low quality components with a classification accuracy of 79%.