Software Requirements Classification and Prioritisation Using Machine Learning

Talele, Pratvina; Phalnikar, Rashmi

doi:10.1007/978-981-15-7106-0_26

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…There are also articles [28] summarizing several machine learning methods and evaluating which ones are more effective in requirement classification. In the method of identifying software vulnerabilities from requirement specifications, in the requirement prioritization method, Talele et al [29] extracted the TF-IDF and BOW features of a requirement odour text and used classification algorithms LR, NB, SVM, DT, and KNN to prioritize requirement odours. Vanamala et al [30] mapped categories from the CWE repository to PROMISE_ In Exp, and machine learning methods were used to identify software vulnerabilities from requirement specifications.…”

Section: Related Workmentioning

confidence: 99%

“…Machine learning has also been widely applied in cost prediction, software testing and software quality assessment in the software development process, such as in consistency research between developers and tasks [21], integration testing [22], software development cost prediction [23] and software quality assessment [24]. Meanwhile, requirements engineering has also applied a large number of machine learning methods [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], such as requirement acquisition, requirement formalization, requirement classification, the identification of software vulnerabilities from requirement specifications, requirement prioritization, requirement dependency extraction and requirement management. Previous studies have demonstrated that the automatic extraction of requirement dependency relationships is a feasible and effective task [32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

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Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

Guan,

Xu,

Cai

2024

Mathematics

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To address the cost and efficiency issues of manually analysing requirement dependency in requirements engineering, a requirement dependency extraction method based on part-of-speech features and an improved stacking ensemble learning model (P-Stacking) is proposed. Firstly, to overcome the problem of singularity in the feature extraction process, this paper integrates part-of-speech features, TF-IDF features, and Word2Vec features during the feature selection stage. The particle swarm optimization algorithm is used to allocate weights to part-of-speech tags, which enhances the significance of crucial information in requirement texts. Secondly, to overcome the performance limitations of standalone machine learning models, an improved stacking model is proposed. The Low Correlation Algorithm and Grid Search Algorithms are utilized in P-stacking to automatically select the optimal combination of the base models, which reduces manual intervention and improves prediction performance. The experimental results show that compared with the method based on TF-IDF features, the highest F1 scores of a standalone machine learning model in the three datasets were improved by 3.89%, 10.68%, and 21.4%, respectively, after integrating part-of-speech features and Word2Vec features. Compared with the method based on a standalone machine learning model, the improved stacking ensemble machine learning model improved F1 scores by 2.29%, 5.18%, and 7.47% in the testing and evaluation of three datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

Guan,

Xu,

Cai

2024

Mathematics

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“…Throughout the software development process, requirement engineering (RE) is essential. Prioritization and requirement identification are the key stages of the RE process [6].…”

Section: For Requirement Engineeringmentioning

confidence: 99%

Review of ways to apply machine learning methods in software engineering

Asaad,

Аvksentieva

2023

E3S Web Conf.

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This article reviews the integration of machine learning (ML) techniques into Software Engineering (SE) across various phases of the software development life cycle (SDLC). The purpose is to investigate the applications of ML in SE, analyze its methodologies, present findings, and draw conclusions regarding its impact. The study categorized ML applications in SE and assessed the performance of various ML algorithms. Authors identified ML applications in SDLC phases, including requirements analysis, design, implementation, testing, and maintenance. ML algorithms, such as supervised and unsupervised learning, are employed for tasks like software requirement identification, design pattern recognition, code generation, and automated testing. In summary, we find that ML-based techniques are experiencing a substantial surge in adoption within the field of software engineering. Nevertheless, it is evident that substantial endeavors are needed to establish thorough comparisons and synergies among these approaches, perform meaningful evaluations grounded in detailed real-world implementations that are applicable to industrial software development. Therefore, our key takeaway is the necessity for a shift in focus towards reproducible research, prioritizing this over isolated novel concepts. Failure to do so may result in the limited practical implementation of these promising applications.

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“…The SDLC is a standardised procedure that tries to guarantee the accuracy of the shipped software in accordance with the client's requirements. A crucial stage in SDLC is requirement engineering, which involves establishing, documenting, and managing system requirements [1]. Requirement elicitation, which is a component of requirement engineering, is the process of col-lecting software requirements by communicating with stakeholders.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated Software Requirements Categorisation using Machine Learning Algorithms

Talele,

Apte,

Phalnikar

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

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Requirement engineering is a mandatory phase of the Software development life cycle (SDLC) that includes defining and documenting system requirements in the Software Requirements Specification (SRS). As the complexity increases, it becomes difficult to categorise the requirements into functional and non-functional requirements. Presently, the dearth of automated techniques necessitates reliance on labour-intensive and time-consuming manual methods for this purpose. This research endeavours to address this gap by investigating and contrasting two prominent feature extraction techniques and their efficacy in automating the classification of requirements. Natural language processing methods are used in the text pre-processing phase, followed by the Term Frequency – Inverse Document Frequency (TF-IDF) and Word2Vec for feature extraction for further understanding. These features are used as input to the Machine Learning algorithms. This study compares existing machine learning algorithms and discusses their correctness in categorising the software requirements. In our study, we have assessed the algorithms Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Neural Network (NN), K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) on the precision and accuracy parameters. The results obtained in this study showed that the TF-IDF feature selection algorithm performed better in categorising requirements than the Word2Vec algorithm, with an accuracy of 91.20% for the Support Vector Machine (SVM) and Random Forest algorithm as compared to 87.36% for the SVM algorithm. A 3.84% difference is seen between the two when applied to the publicly available PURE dataset. We believe these results will aid developers in building products that aid in requirement engineering.

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Software Requirements Classification and Prioritisation Using Machine Learning

Cited by 7 publications

References 20 publications

Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

Requirement Dependency Extraction Based on Improved Stacking Ensemble Machine Learning

Review of ways to apply machine learning methods in software engineering

Semi-automated Software Requirements Categorisation using Machine Learning Algorithms

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