- author = "Wisam Haitham Abbood {Al-Zubaidi}",
- title = "Multi-objective search-based approach for software project management",
- school = "University of Wollongong",
- year = "2019",
- address = "Wollongong, NSW 2522, Australia",
- month = "31 " # mar,
- keywords = "genetic algorithms, genetic programming, SBSE, Iteration Planning, Agile Development, Effort Estimation, MOGP",
-
URL = "
https://ro.uow.edu.au/theses1/690/",
-
URL = "
https://ro.uow.edu.au/theses1/690/Al-Zubaidi_thesis.pdf",
-
URL = "https://ro.uow.edu.au/cgi/viewcontent.cgi?article=1678&context=theses1",
- size = "254 pages",
-
abstract = "Project management covers the entire lifecycle of
software, underpinning the success or failure of many
software projects. Managing modern software projects
often follows the incremental and iterative process
where a software product is incrementally developed
through a number of iterations. In each iteration, the
development team needs to complete a number of issues,
each of which can be implementing a new feature for the
software, modifying an existing functionality, fixing a
bug or conducting some other project tasks. Although
this agile approach reduces the risk of project
failures, managing projects at the level of issues and
iterations is still highly difficult due to the
inherent dynamic nature of software, especially in
large-scale software projects. Challenges in this
context can be in many forms such as making accurate
estimations of the resolution time and effort of
resolving issues or selecting suitable issues for
upcoming iterations. These integral parts of planning
is highly challenging since many factors need
considering such as customer business value and the
team historical estimations, capability and
performance. Challenges also exist at the
implementation level, such as managing the reviewing of
code changes made to resolve issues. There is currently
a serious lack of automated support which help project
managers and software development teams address those
challenges.
This thesis aims to fill those gaps. We leverage a huge amount of historical data in software projects to generate valuable insight for dealing with those challenges in managing iterations and issues. We reformulate those project management problems as search-based optimization problems and employ a range of evolutionary meta-heuristics search techniques to solve them. The search is simultaneously guided by a number of multiple fitness functions that express different objectives (e.g. customer business value, developer expertise and workload, and complexity of estimation models) and constraints (e.g. a team historical capability and performance) in the context of modern software projects. Using this approach, we build novel models for estimating issue resolution time and effort, suggesting appropriate issues for upcoming iterations in iteration planning and recommending suitable reviewers for code changes made to resolve issues. An extensive empirical evaluation on a range of large software projects (including Mesos, Usergrid, Aurora, Slider, Kylin, Mahout, Common, Hdfs, MapReduce, Yarn, Apstud, Mule, Dnn, Timob, Tisud, Xd, Nexus, Android, LibreOffice, Qt, and Openstack) demonstrates the highly effective performance of our approach against other alternative techniques (improvement between 1.83 to 550 percent) to show the effectiveness of our approach.",
- notes = "supervisor: Hoa Khanh Dam",
