Ambiguous requirements: A semi-automated approach to identify and clarify ambiguity in large-scale projects

Asadabadi, Mehdi Rajabi; Saberi, Morteza; Zwikael, Ofer; Chang, Elizabeth

doi:10.1016/j.cie.2020.106828

Cited by 11 publications

(2 citation statements)

References 33 publications

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“…There have been various machine learning applications over the software requirement datasets. For instance, Asabadi et al [8] employed fuzzy set theory to identify ambiguous SRS statements by using an ambiguous terms list. Apart from machine learning methods, rule-based approaches have also been employed for the classification tasks in the software engineering domain such as requirement classification [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Fine-tuning for Multiclass Classification over Software Requirement Data

Çevik

Yıldırım

Parikh

et al. 2023

Preprint

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The analysis of software requirement specifications (SRS) using Natural Language Processing (NLP) methods has been an important study area in the software engineering field in recent years. Especially thanks to the advances brought by deep learning and transfer learning approaches in NLP, SRS data can be utilized for various learning tasks more easily. In this study, we employ a three-stage domain-adaptive fine-tuning approach for three prediction tasks regarding software requirements, which improve the model robustness on a real distribution shift. The multi-class classification tasks involve predicting the type, priority and severity of the requirement texts specified by the users. We compare our results with strong classification baselines such as word embedding pooling and Sentence BERT, and show that the adaptive fine-tuning leads to performance improvements across the tasks. We find that an adaptively fine-tuned model can be specialized to particular data distribution, which is able to generate accurate results and learns from abundantly available textual data in software engineering task management systems.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptive Fine-tuning for Multiclass Classification over Software Requirement Data

Çevik

Yıldırım

Parikh

et al. 2023

Preprint

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“…According to previous studies, the correct requirements classification of requirements and clear definition of requirements have been the main focuses of researchers to allow filtering and prioritizing of requirements. There are numerous algorithms, including Term Frequency -Inverse Document Frequency (TF-IDF) (Wein and Briggs, 2021;Dias-Canedo and Cordeiro-Mendes, 2020) and machine learning techniques like Support Vector Machine (SVM) (Shariff, 2021;Kurtanović and Maalej, 2017), Naïve Bayes (NB) (Shariff, 2021), Logistic Regression (LR) (Dias-Canedo and Cordeiro-Mendes, 2020) and Natural Language Processing (NLP) (Wein and Briggs, 2021;Asadabadi et al, 2020;Aysolmaz et al, 2018;Kurtanović & Maalej, 2017;Emebo, Olawande, and Charles, 2016), have been implemented in various requirements management tasks to analyze and classify requirements by going through requirements normalization, feature extraction, feature selection, and finally classification.…”

Section: Introductionmentioning

confidence: 99%

ReqGo: A Semi-Automated Requirements Management Tool

Koh¹,

Chua²

2023

IJTech

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This study deals with issues of changes in requirements management by dealing with requirements ambiguity and prioritization. A hypothesis about the possibility of integrating machine learning techniques and requirements management processes has been proven. It highlights the efforts in automating requirements ambiguity identification, requirements classification, and prioritization considering multi-criteria in decision-making through the utilization of Natural Language Processing (NLP) techniques and Universal Sentence Encoder. Naïve Bayes (NB) classifier has been applied with its remarkable performance on binarily classifying requirements. Although existing methods proved to improve one or two of the process significantly, it rarely integrates the whole requirements management activity. The proposed tool helps the development team to manage the requirements systematically. The prioritization algorithm is proved to work as expected by considering multiple constraints before calculating the priority value. Meanwhile, it identifies the ambiguity that exists in the requirement automatically. The ambiguity classifier successfully identifies 87.5% of requirements accurately. Possible future work could be done in improving the prioritization module by allowing automated estimation of priority value upon requirements change. Future work may extend the automation coverage by providing test case generation.

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