User directed search based reverse engineering
| aut.embargo | No | en |
| aut.supplementaryupload | Yes | |
| aut.thirdpc.contains | No | |
| aut.thirdpc.permission | No | |
| aut.thirdpc.removed | No | |
| dc.contributor.advisor | Connor, Andy | |
| dc.contributor.advisor | MacDonnell, Stephen | |
| dc.contributor.author | Schmidt, Frederik | |
| dc.date.accessioned | 2010-03-26T03:46:53Z | |
| dc.date.available | 2010-03-26T03:46:53Z | |
| dc.date.copyright | 2009 | |
| dc.date.issued | 2009 | |
| dc.date.updated | 2010-03-26T03:41:03Z | |
| dc.description.abstract | The current research represents the planning, design, implementation and evaluation of a user directed software clustering approach that utilizes Search Based Software Engineering (SBSE). The aim of this research is to examine if a user directed software clustering approach contributes to the quality of software clustering. Because of the explorative and constructive character this research project utilises the System Development Research Methodology. This research is enabled by the implementation of the Search Based Reverse Engineering (SBRE) component. The SBRE component features multiple similarity measurements and the inclusion of user constraints in the clustering process to create different implementation perspectives of the software system depending on the requirements and preferences of the stakeholders. These similarity measurements are based on software metrics, which measure different software-attributes. The SBRE component utilizes a greedy and tabu search algorithm for the identification of the cluster landscape of the analyzed software systems. The evaluation showed that a user controlled SBSE cluster approach is able to adapt to different user configurations and derive corresponding cluster landscapes from software systems. Different measures are introduced to control the cluster process. It has been shown how these measures contribute to the quality of the clustering. It is demonstrated that tabu search is applicable in the field of software clustering. Finally, it has been examined that a multiple metric approach allows adapting the clustering process to the requirements of the stakeholders and the design of the software system to optimize the clustering result. | |
| dc.identifier.uri | https://hdl.handle.net/10292/836 | |
| dc.language.iso | en | en |
| dc.publisher | Auckland University of Technology | |
| dc.rights.accessrights | OpenAccess | |
| dc.subject | Search based software engineering | |
| dc.subject | Reverse engineering | |
| dc.subject | SBSE | |
| dc.subject | Software engineering | |
| dc.subject | Java | |
| dc.title | User directed search based reverse engineering | |
| dc.type | Thesis | |
| thesis.degree.grantor | Auckland University of Technology | |
| thesis.degree.level | Masters Theses | |
| thesis.degree.name | Master of Computer and Information Sciences |