The Smart Product Backlog: A Classification Model of User Stories
In agile software development processes, user stories (US) had been used to specify application functionalities from the users’ perspective. For intelligent applications leveraging artificial intelligence (AI), the Smart Product Backlog (SPB) has included both AI-implementable and non-AI...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10714400/ |
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| author | Mauricio Gaona-Cuevas Victor Bucheli Guerrero Fredy H. Vera-Rivera |
| author_facet | Mauricio Gaona-Cuevas Victor Bucheli Guerrero Fredy H. Vera-Rivera |
| author_sort | Mauricio Gaona-Cuevas |
| collection | DOAJ |
| description | In agile software development processes, user stories (US) had been used to specify application functionalities from the users’ perspective. For intelligent applications leveraging artificial intelligence (AI), the Smart Product Backlog (SPB) has included both AI-implementable and non-AI functionalities. This paper had proposed a model employing supervised machine learning techniques to classify US descriptions based on their technical feasibility for AI implementation. This model had aimed to assist in constructing smart product backlogs (SPB). Classifying US in agile development, particularly with AI, had been a labor-intensive process demanding significant time and expert involvement. Additionally, the lack of a dedicated dataset for this task had limited the applicability of automated methods. This study addressed this challenge by having experts classify the Mendeley US dataset using binary classification (AI and non-AI). The proposal had involved developing an automatic classification model to process US descriptions and distinguish those suitable for AI implementation. This model had leveraged advanced text processing techniques to refine the textual features of the US. Additionally, it had employed three binary classification techniques: logistic regression, k-nearest neighbors (k-NN), and support vector machines (SVM). The model’s performance had been evaluated using metrics such as accuracy, loss (Log-Likelihood Loss), precision, recall, F1 score, area under the ROC curve, and specificity to identify the best-performing technique in binary classification. Logistic regression and SVM models had demonstrated high accuracy, with scores of 0.748 and 0.740, respectively. These results had highlighted the potential of an automated tool for recommending US feasible for AI development, thereby supporting decision-making in agile software projects. |
| format | Article |
| id | doaj-art-b296076d400a4e088ba4be8be2061946 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b296076d400a4e088ba4be8be20619462025-08-20T01:47:50ZengIEEEIEEE Access2169-35362024-01-011215000815001910.1109/ACCESS.2024.347883310714400The Smart Product Backlog: A Classification Model of User StoriesMauricio Gaona-Cuevas0https://orcid.org/0000-0003-1303-2207Victor Bucheli Guerrero1https://orcid.org/0000-0002-0885-8699Fredy H. Vera-Rivera2Escuela de Ingeniería de Sistemas y Computación, Facultad de Ingeniería, Universidad del Valle, Cali, ColombiaEscuela de Ingeniería de Sistemas y Computación, Facultad de Ingeniería, Universidad del Valle, Cali, ColombiaGrupo de Investigación en Inteligencia Artificial (GIA), Departamento de Sistemas, Universidad Francisco de Paula Santander, Cúcuta, ColombiaIn agile software development processes, user stories (US) had been used to specify application functionalities from the users’ perspective. For intelligent applications leveraging artificial intelligence (AI), the Smart Product Backlog (SPB) has included both AI-implementable and non-AI functionalities. This paper had proposed a model employing supervised machine learning techniques to classify US descriptions based on their technical feasibility for AI implementation. This model had aimed to assist in constructing smart product backlogs (SPB). Classifying US in agile development, particularly with AI, had been a labor-intensive process demanding significant time and expert involvement. Additionally, the lack of a dedicated dataset for this task had limited the applicability of automated methods. This study addressed this challenge by having experts classify the Mendeley US dataset using binary classification (AI and non-AI). The proposal had involved developing an automatic classification model to process US descriptions and distinguish those suitable for AI implementation. This model had leveraged advanced text processing techniques to refine the textual features of the US. Additionally, it had employed three binary classification techniques: logistic regression, k-nearest neighbors (k-NN), and support vector machines (SVM). The model’s performance had been evaluated using metrics such as accuracy, loss (Log-Likelihood Loss), precision, recall, F1 score, area under the ROC curve, and specificity to identify the best-performing technique in binary classification. Logistic regression and SVM models had demonstrated high accuracy, with scores of 0.748 and 0.740, respectively. These results had highlighted the potential of an automated tool for recommending US feasible for AI development, thereby supporting decision-making in agile software projects.https://ieeexplore.ieee.org/document/10714400/User stories classificationsmart product backlogsmart user story identifierrequirements elicitationagile software development (ASD)machine learning |
| spellingShingle | Mauricio Gaona-Cuevas Victor Bucheli Guerrero Fredy H. Vera-Rivera The Smart Product Backlog: A Classification Model of User Stories IEEE Access User stories classification smart product backlog smart user story identifier requirements elicitation agile software development (ASD) machine learning |
| title | The Smart Product Backlog: A Classification Model of User Stories |
| title_full | The Smart Product Backlog: A Classification Model of User Stories |
| title_fullStr | The Smart Product Backlog: A Classification Model of User Stories |
| title_full_unstemmed | The Smart Product Backlog: A Classification Model of User Stories |
| title_short | The Smart Product Backlog: A Classification Model of User Stories |
| title_sort | smart product backlog a classification model of user stories |
| topic | User stories classification smart product backlog smart user story identifier requirements elicitation agile software development (ASD) machine learning |
| url | https://ieeexplore.ieee.org/document/10714400/ |
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