Story Point Estimation Using Issue Reports With Deep Attention Neural Network

Kassem, Haithem; Mahar, Khaled; Saad, Amani A.

doi:10.37190/e-Inf230104

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Tytuł artykułu

Story Point Estimation Using Issue Reports With Deep Attention Neural Network

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Kassem Haithem , Mahar Khaled , Saad Amani A.

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DOI

10.37190/e-Inf230104

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Abstrakty

Background: Estimating the effort required for software engineering tasks is incredibly tricky, but it is critical for project planning. Issue reports are frequently used in the agile community to describe tasks, and story points are used to estimate task effort. Aim: This paper proposes a machine learning regression model for estimating the number of story points needed to solve a task. The system can be trained from raw input data to predict outcomes without the need for manual feature engineering. Method: Hierarchical attention networks are used in the proposed model. It has two levels of attention mechanisms implemented at word and sentence levels. The model gradually constructs a document vector by grouping significant words into sentence vectors and then merging significant sentence vectors to create document vectors. Then, the document vectors are fed into a shallow neural network to predict the story point. Results: The experiments show that the proposed approach outperforms the state-of-the-art technique Deep-S which uses Recurrent Highway Networks. The proposed model has improved Mean Absolute Error (MAE) by an average of 16.6% and has improved Median Absolute Error (MdAE) by an average of 53%. Conclusion: An empirical evaluation shows that the proposed approach outperforms the previous work.

Słowa kluczowe

story points deep learning glove hierarchical attention networks agile planning poker

Wydawca

Oficyna Wydawnicza Politechniki Wrocławskiej

Czasopismo

e-Informatica Software Engineering Journal

Rocznik

2023

Tom

Vol. 17, nr 1

Strony

art. no. 230104

Opis fizyczny

Bibliogr. 71 poz., rys., tab.

Twórcy

autor

Kassem Haithem

haithem_k@aast.edu

Multimedia Center, AASTMT, Alex, Egypt

autor

Mahar Khaled

khmahar@aast.edu

College of Computing and Information Technology, AASTMT, Alex, Egypt

autor

Saad Amani A.

amani.saad@aast.edu

College of Engineering and Technology, AASTMT, Alex, Egypt

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

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