The Impact of Instance Selection Algorithms on Maintenance Effort Estimation for Open-Source Software

Abran, Alain; Cheikhi, Laila; Idri, Ali

doi:10.1007/978-3-031-04829-6_17

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…Under the topic of the Resolution Time Prediction system, the application of ML plays an essential role in making this system possible and successful [11], [12], [13]. A few ML algorithms are commonly used in the Resolution Time Prediction system, including Decision Tree [5], [14] Random Forest [9], [15] and Gradient Boosting [16].…”

Section: Methodsmentioning

confidence: 99%

Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Tong-Ern,

Su-Cheng,

Kok-Why

et al. 2024

JOIV : Int. J. Inform. Visualization

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The quality of customer service emphasizes support tickets. An excellent support ticket system qualifies businesses to provide clients with the finest level of customer support. This enables enterprises to guarantee the consistency of quality customer service delivered successfully, ensuring all clients have a good experience regardless of the nature of their inquiry or issue. To further achieve a higher efficiency of resource allocation, this is when the prediction of ticket resolution time comes into place. The advancing technologies, including artificial intelligence (AI) and machine learning (ML), can perform predictions on the duration required to tackle specific problems based on past similar data. ML enables the possibility of automatically classifying tickets, making it possible to anticipate the time resolution for cases. This paper explores various ML techniques widely applied in the Resolution Time Prediction system and investigates the performance of three selected ML techniques via the benchmarking dataset obtained from the UCI Machine Learning Repository. Implementing selected techniques will involve creating a graphical user interface and data visualization to provide insight for data analysis. The best technique will be concluded after performing the ML technique evaluation. The evaluation metrics involved in this step include Root Mean Square Error (RMSE) and Root Mean Absolute Error (MAE). The experimental evaluation shows that the best performance among the selected ML techniques is Random Forest (RF).

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Section: Methodsmentioning

confidence: 99%

Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Tong-Ern,

Su-Cheng,

Kok-Why

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Maintenance effort and software maintainability are tightly linked. A software system's maintainability value decreases with increasing maintenance effort (Miloudi et al, 2022). A software maintainability prediction model will assist organizations in accurately predicting the maintenance of their software systems, allowing them to better allocate their resources and make software maintenance‐related decisions (Malhotra & Lata, 2022).…”

Section: Introductionmentioning

confidence: 99%

A review on soft computing approaches for predicting maintainability of software: State‐of‐the‐art, technical challenges, and future directions

Yenduri

Gadekallu

2023

Expert Systems

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The software is changing rapidly with the invention of advanced technologies and methodologies. The ability to rapidly and successfully upgrade software in response to changing business requirements is more vital than ever. For the long‐term management of software products, measuring software maintainability is crucial. The use of soft computing techniques for software maintainability prediction has shown immense promise in software maintenance process by providing accurate prediction of software maintainability. To better understand the role of soft computing techniques for software maintainability prediction, we aim to provide a systematic literature review of soft computing techniques for predicting software maintainability. Firstly, we provide a detailed overview of software maintainability. Following this, we explore the fundamentals of software maintainability and the reasons for adopting soft computing methodologies for predicting software maintainability. Later, we examine the soft computing approaches employed in the process of software maintainability prediction. Furthermore, we discuss the difficulties and potential solutions associated with the use of soft computing techniques in predicting maintainability of software. Finally, we conclude the review with some promising future directions to drive further research innovations and developments in this promising area. This systematic literature review provides a comprehensive overview of the soft computing strategies utilized for software maintainability for future researchers.

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The Impact of Grid Search on Bug Resolution Prediction for Open-Source Software

Miloudi,

Cheikhi,

Idri

et al. 2023

2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)

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The Impact of Instance Selection Algorithms on Maintenance Effort Estimation for Open-Source Software

Cited by 3 publications

References 21 publications

Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

Performance Evaluation on Resolution Time Prediction Using Machine Learning Techniques

A review on soft computing approaches for predicting maintainability of software: State‐of‐the‐art, technical challenges, and future directions

The Impact of Grid Search on Bug Resolution Prediction for Open-Source Software

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