Computing and Information Systems - Theses

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Type: PhD thesis × Start date: 2020 × End date: 2022 × Subject: Machine Learning × Subject: Agile ×

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    Improving Agile Sprint Planning Through Empirical Studies of Documented Information and Story Points Estimation
    Pasuksmit, Jirat ( 2022)
    In Agile iterative development (e.g., Scrum), effort estimation is an integral part of the development iteration planning (i.e., sprint planning). Unlike traditional software development teams, an Agile team relies on a lightweight estimation method based on the team consensus (e.g., Planning Poker) for effort estimation and the estimated effort is continuously refined (or changed) to improve the estimation accuracy. However, such lightweight estimation methods are prone to be inaccurate and late changes of the estimated effort may cause the sprint plan to become unreliable. Despite a large body of research, only few studies have reviewed the reasons for inaccurate estimations and the approaches to improve effort estimation. We conducted a systematic literature review and found that the quality of the available information is one of the most common reasons for inaccurate estimations. We found several manual approaches aim to help the team improve the information quality and manage the uncertainty in effort estimation. However, prior work reported that the practitioners were reluctant to use them as they added additional overhead to the development process. The goals of this thesis are to better understand and propose the approaches that help the team achieves accurate estimation without introducing additional overhead. To achieve this goal, we conducted studies in this thesis in two broad areas. We first conducted two empirical studies to investigate the importance of documented information for effort estimation and the impact of estimation changes in a project. In the first empirical study, we aim to investigate the importance and quality of documented information for effort estimation. We conducted a survey study with 121 Agile practitioners from 25 countries. We found that the documented information is considered important for effort estimation. We also found that the useful documented information for effort estimation is often changed and the practitioners would re-estimate effort when the change of documented information occurred, even after the work had started. In the second empirical study, we aim to better understand the change of effort (in Story Points unit; SP). We examined the prevalence of SP changes, the accuracy of changed SP, and the impact of information changes on SP changes. We found that the SP were not often changed after sprint planning. However, when the SP were changed, the changing size was relatively large and the changed SP may be inaccurate. We also found that the SP changes were often occurred along with the information changes for scope modification. These findings suggest that a change of documented information could lead to a change of effort, and the changed effort could have a large impact on the sprint plan. To mitigate the risk of an unreliable sprint plan, the documented information and the estimated effort should be verified and stabilized before finalizing the sprint plan. Otherwise, the team may have to re-estimate the effort and adjust the sprint plan. However, revisiting all documented information and estimated SP could be a labor-intensive task and may not comply with the Agile principles. To help the team manages these uncertainties without introducing additional overhead, we proposed the automated approaches called DocWarn and SPWarn to predict the documentation changes and SP changes that may occur after sprint planning. We built DocWarn and SPWarn using machine learning and deep learning techniques based on the metrics that measure the characteristics of the work items. We evaluated DocWarn and SPWarn using the work items extracted from the open-source projects. Our empirical evaluations show that DocWarn achieved an average AUC of 0.75 and SPWarn achieved an average AUC of 0.73, which are significantly higher than baseline models. These results suggest that our approaches can predict future changes of documented information and SP based on the currently-available information. With our approaches, the team will be better aware and pay attention to the potential documentation changes and SP changes during sprint planning. Thus, the team can manage uncertainty and reduce the risk of unreliable effort estimation and sprint planning without additional overhead.