- author = "Wasif Afzal",
- title = "Search-Based Approaches to Software Fault Prediction and Software Testing",
- school = "School of Engineering, Dept. of Systems and Software Engineering, Blekinge Institute of Technology",
- year = "2009",
- type = "Licentiate Dissertation",
- address = "Sweden",
- keywords = "genetic algorithms, genetic programming, SBSE, Software Engineering, Computer Science, Artificial Intelligence",
-
URL = "
http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3/$file/Afzal_lic.pdf",
- broken = "http://www.bth.se/fou/forskinfo.nsf/all/f0738b5fc4ca0bbac12575980043def3?OpenDocument",
- size = "212 pages",
- isbn13 = "978-91-7295-163-1",
- language = "eng",
- oai = "oai:bth.se:forskinfoF0738B5FC4CA0BBAC12575980043DEF3",
-
abstract = "Software verification and validation activities are
essential for software quality but also constitute a
large part of software development costs. Therefore
efficient and cost-effective software verification and
validation activities are both a priority and a
necessity considering the pressure to decrease
time-to-market and intense competition faced by many,
if not all, companies today. It is then perhaps not
unexpected that decisions related to software quality,
when to stop testing, testing schedule and testing
resource allocation needs to be as accurate as
possible.
This thesis investigates the application of search-based techniques within two activities of software verification and validation: Software fault prediction and software testing for non-functional system properties. Software fault prediction modeling can provide support for making important decisions as outlined above. In this thesis we empirically evaluate symbolic regression using genetic programming (a search-based technique) as a potential method for software fault predictions. Using data sets from both industrial and open-source software, the strengths and weaknesses of applying symbolic regression in genetic programming are evaluated against competitive techniques. In addition to software fault prediction this thesis also consolidates available research into predictive modeling of other attributes by applying symbolic regression in genetic programming, thus presenting a broader perspective. As an extension to the application of search-based techniques within software verification and validation this thesis further investigates the extent of application of search-based techniques for testing non-functional system properties.
Based on the research findings in this thesis it can be concluded that applying symbolic regression in genetic programming may be a viable technique for software fault prediction. We additionally seek literature evidence where other search-based techniques are applied for testing of non-functional system properties, hence contributing towards the growing application of search-based techniques in diverse activities within software verification and validation.",
