Recent Advances in Software Reliability Assurance

Achieving true service agility requires development teams to be able to continuously integrate and deliver software every few weeks. This requires capabilities for test automation, modelling and prediction of software reliability. In this paper, we report on our recent experiences applying a simple and novel curve shifting technique to software defect prediction for a continuous integration, continuous delivery project. The technique transforms the defect arrival curve from a given previous release using the user story (or feature)1 development plan so as to predict defect arrival for the required release. We also discuss the different views on software defects from a quality vis-a-vis project management perspective and how the proposed technique applies to either.

[1]  Filip De Turck,et al.  Network Function Virtualization: State-of-the-Art and Research Challenges , 2015, IEEE Communications Surveys & Tutorials.

[2]  Javaid Iqbal,et al.  Software reliability growth models: A comparison of linear and exponential fault content functions for study of imperfect debugging situations , 2017 .

[3]  Amrit L. Goel,et al.  Time-Dependent Error-Detection Rate Model for Software Reliability and Other Performance Measures , 1979, IEEE Transactions on Reliability.

[4]  Rashid Mijumbi,et al.  BRACE: Cloud-Based Software Reliability Assurance , 2017, 2017 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).

[5]  John D. Musa,et al.  Software reliability - measurement, prediction, application , 1987, McGraw-Hill series in software engineering and technology.