Inlämning av Examensarbete / Submission of Thesis

Qui Can Cuong Tran , pp. 72. COM/School of Computing, 2012.

The work

Författare / Author: Qui Can Cuong Tran
longphithien@gmail.com
Titel / Title: Empirical evaluation of defect identification indicators and defect prediction models
Översatt titel / Translated title: Empirical evaluation of defect identification indicators and defect prediction models
Abstrakt Abstract:

Context. Quality assurance plays a vital role in the software engineering development process. It can be considered as one of the activities, to observe the execution of software project to validate if it behaves as expected or not. Quality assurance activities contribute to the success of software project by reducing the risks of software’s quality. Accurate planning, launching and controlling quality assurance activities on time can help to improve the performance of software projects.
However, quality assurance activities also consume time and cost. One of the reasons is that they may not focus on the potential defect-prone area. In some of the latest and more accurate findings, researchers suggested that quality assurance activities should focus on the scope that may have the potential of defect; and defect predictors should be used to support them in order to save time and cost. Many available models recommend that the project’s history information be used as defect indicator to predict the number of defects in the software project.
Objectives. In this thesis, new models are defined to predict the number of defects in the classes of single software systems. In addition, the new models are built based on the combination of product metrics as defect predictors.
Methods. In the systematic review a number of article sources are used, including IEEE Xplore, ACM Digital Library, and Springer Link, in order to find the existing models related to the topic. In this context, open source projects are used as training sets to extract information about occurred defects and the system evolution. The training data is then used for the definition of the prediction models. Afterwards, the defined models are applied on other systems that provide test data, so information that was not used for the training of the models; to validate the accuracy and correctness of the models
Results. Two models are built. One model is built to predict the number of defects of one class. One model is built to predict whether one class contains bug or no bug..
Conclusions. The proposed models are the combination of product metrics as defect predictors that can be used either to predict the number of defects of one class or to predict if one class contains bugs or no bugs. This combination of product metrics as defect predictors can improve the accuracy of defect prediction and quality assurance activities; by giving hints on potential defect prone classes before defect search activities will be performed. Therefore, it can improve the software development and quality assurance in terms of time and cost

Ämnesord / Subject: Datavetenskap - Computer Science\Software Engineering
Datavetenskap - Computer Science\Software Engineering
Datavetenskap - Computer Science\Software Engineering
Nyckelord / Keywords: defect prediction, defect indicators, defect prediction models, quality assurance

Publication info

Dokument id / Document id: houn-8sjkj2
Program:/ Programme European Master on Software Engineering
Registreringsdatum / Date of registration: 03/19/2012
Uppsatstyp / Type of thesis: Masterarbete/Master's Thesis (120 credits)

Context

Handledare / Supervisor: Wasif Afzal
wasif.afzal@bth.se
Examinator / Examiner: Tony Gorschek
Organisation / Organisation: Blekinge Institute of Technology
Institution / School: COM/School of Computing

+46 455 38 50 00
I samarbete med / In co-operation with: Fraunhofer Institute for Experimental Software Engineering, in Kaiserslautern, Germany

Files & Access

Bifogad uppsats fil(er) / Files attached: bth2012quican.pdf (2677 kB, öppnas i nytt fönster)