Multi-objective search-based approach to estimate issue resolution time

Al-Zubaidi, Wisam Haitham Abbood; Dam, Hoa Khanh; Ghose, Aditya; Li, Xiaodong

doi:10.1145/3127005.3127011

Cited by 11 publications

(7 citation statements)

References 33 publications

(70 reference statements)

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“…Regarding predictions at the level of individual tasks or issues, there are some studies that employ ML [21] and search-based [22] approaches to predict the resolution time of newly reported issues or bugs, primarily during maintenance stages.…”

Section: Related Workmentioning

confidence: 99%

Applying Machine Learning to Estimate the Effort and Duration of Individual Tasks in Software Projects

Sousa,

Veloso,

Gonçalves

et al. 2023

IEEE Access

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CONTEXT: Software estimation is a vital yet challenging project management activity. Various methods, from empirical to algorithmic, have been developed to fit different development contexts, from plandriven to agile. Recently, machine learning techniques have shown potential in this realm but are still underexplored, especially for individual task estimation. OBJECTIVES: We investigate the use of machine learning techniques in predicting task effort and duration in software projects to assess their applicability and effectiveness in production environments, identify the best-performing algorithms, and pinpoint key input variables (features) for predictions. METHOD: We conducted experiments with datasets of various sizes and structures exported from three project management tools used by partner companies. For each dataset, we trained regression models for predicting the effort and duration of individual tasks using eight machine learning algorithms. The models were validated using k-fold cross-validation and evaluated with several metrics. RESULTS: Ensemble algorithms like Random Forest, Extra Trees Regressor, and XGBoost consistently outperformed non-ensemble ones across the three datasets. However, the estimation accuracy and feature importance varied significantly across datasets, with a Mean Magnitude of Relative Error (MMRE) ranging from 0.11 to 9.45 across the datasets and target variables. Nevertheless, even in the worst-performing dataset, effort estimates aggregated to the project level showed good accuracy, with MMRE = 0.23. CONCLUSIONS: Machine learning algorithms, especially ensemble ones, seem to be a viable option for estimating the effort and duration of individual tasks in software projects. However, the quality of the estimates and the relevant features may depend largely on the characteristics of the available datasets and underlying projects. Nevertheless, even when the accuracy of individual estimates is poor, the aggregated estimates at the project level may present a good accuracy due to error compensation.

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Section: Related Workmentioning

confidence: 99%

Applying Machine Learning to Estimate the Effort and Duration of Individual Tasks in Software Projects

Sousa,

Veloso,

Gonçalves

et al. 2023

IEEE Access

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“…"Multi-objective Search-Based Approach to Estimate Issue Resolution Time," by Wisam Al-Zubaidi and his colleagues, presents an approach to estimate the time required to resolve an issue. 4 These estimates can be used to inform…”

Section: Estimating Issue Resolution Timementioning

confidence: 99%

Empirical Software Engineering, Predictive Models, and Product Lines

et al. 2018

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THIS ISSUE'S COLUMN reports on papers presented at the 11th Inter national Symposium on Empirical Software Engineering and Measurement (ESEM 17), 13th International Conference on Predictive Models and Data Analytics in Software Engineering (PROMISE 17), and 21st International Systems and Software Product Line Conference (SPLC 17). Feedback and suggestions are welcome. In addition, if you try or adopt any of the practices included in the column, please send Jeffrey Carver and the paper authors a note about your experiences.

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“…Another critical activity from maintenance planning is allocating resources to handle bugs associated with each opened bug report, which could be significant [5]. Estimating the "bug fix time" is essential for many stakeholders [6,7]. For software managers, it is one of the main factors that helps them to perform such allocation more effectively [8,9,10].…”

Section: Introductionmentioning

confidence: 99%