Wasif Afzal | Blekinge Institute of Technology (original) (raw)

Supervisors: Richard Torkar and Robert Feldt

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In the presence of a number of algorithms for classification and prediction in software engineeri... more In the presence of a number of algorithms for classification and prediction in software engineering, there is a need to have a systematic way of assessing their performances. The performance assessment is typically done by some form of partitioning or resampling of the original data to alleviate biased estimation. For predictive and classification studies in software engineering, there is a lack of a definitive advice on the most appropriate resampling method to use.

ABSTRACT Software verification and validation (V&V) activities are critical for achieving softwar... more ABSTRACT Software verification and validation (V&V) activities are critical for achieving software quality; however, these activities also constitute a large part of the costs when developing software. Therefore efficient and effective software V&V activities are both a priority and a necessity considering the pressure to decrease time-to-market and the intense competition faced by many, if not all, companies today.

Asia-Pacific Software Engineering Conference- …

... A Systematic Review of Non-Functional Search-Based Software Testing (2008). Download: http://... more ... A Systematic Review of Non-Functional Search-Based Software Testing (2008). Download: http://drfeldt.googlepages.com/afzal_submitted0805 CACHED: Download as a PDF. by Wasif Afzal , Richard Torkar , Robert Feldt. Add ...

2nd International Symposium on Search Based …, Jan 1, 2010

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Wasif Afzal Prasanth Anbalagan Dejan Baca Christian Bird Peter Bokor Olof Bridal Erwan Brottier J... more Wasif Afzal Prasanth Anbalagan Dejan Baca Christian Bird Peter Bokor Olof Bridal Erwan Brottier Jeremy Bruce Brett Daniel Romain Delamare Hongbo Deng Cecilia Ekelin Marwa El-Ghali Anuraj Ennai Robert Feldt Harald Gall Michael Gegick László Gönczy Madhu Gopinathan Bhargav Gulavani Sarah Heckman Martin Hiller Chih-Wei Ho Chris Hobbs Kaizhu Huang Vilas Jagannath Sang-Uk Jeon Sheetal Kalyani Sarfraz Khurshid Mitsuhiro Kimura Zhifeng Lai Steven Lauterburg Yun Young Lee Otávio Lemos Henrik Lönn Hao Ma István Majzik Cesar Marcondes

Building reliability growth models to predict software reliability and identify and remove errors... more Building reliability growth models to predict software reliability and identify and remove errors is both a necessity and a challenge for software testing engineers and project managers. Being able to predict the number of faults in software helps significantly in determining the software release date and in effectively managing project resources. Most of the growth models consider two or three parameters to estimate the accumulated faults in the testing process. Interest in using evolutionary computation to solve prediction and modeling problems has grown in recent years. In this paper, we explore the use of genetic programming (GP) as a tool to help in building growth models that can accurately predict the number of faults in software early on in the testing process. The proposed GP model is based on a recursive relation derived from the history of measured faults. The developed model is tested on real-time control, military, and operating system applications. The dataset was developed by John Musa of Bell Telephone Laboratories. The results of a comparison of the GP model with the traditional and simpler auto-regression model are presented.