Tawosi, Vali;
(2023)
Predictiveness and Effectiveness of Story Points in Agile Software Development.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
Agile Software Development (ASD) is one of the most popular iterative software development methodologies, which takes a different approach from the conventional sequential methods. Agile methods promise a faster response to unanticipated changes during development, typically contrasted with traditional project development, which assumes that software is specifiable and predictable. Traditionally, practitioners and researchers have utilised different Functional Size Measures (FSMs) as the main cost driver to estimate the effort required to develop a project (Software Effort Estimation – SSE). However, FSM methods are not easy to use with ASD. Thus, another measure, namely Story Point (SP), has become popular in this context. SP is a relative unit representing an intuitive mixture of complexity and the required effort of a user requirement. Although recent surveys report on a growing trend toward intelligent effort estimation techniques for ASD, the adoption of these techniques is still limited in practice. Several factors limit the accuracy and adaptability of these techniques. The primary factor is the lack of enough noise-free information at the estimation time, restricting the model’s accuracy and reliability. This thesis concentrates on SEE for ASD from both the technique and data perspectives. Under this umbrella, I first evaluate two prominent state-of-the-art works for SP estimation to understand their strengths and weaknesses. I then introduce and evaluate a novel method for SP estimation based on text clustering. Next, I investigate the relationship between SP and development time by conducting a thorough empirical study. Finally, I explore the effectiveness of SP estimation methods when used to estimate the actual time. To carry out this research, I have curated the TAWOS (Tawosi Agile Web-based Open-Source) dataset, which consists of over half a million issues from Agile, open-source projects. TAWOS has been made publicly available to allow for reproduction and extension in future work.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Predictiveness and Effectiveness of Story Points in Agile Software Development |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2023. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10175111 |
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