Data-Driven Elicitation and Optimization of Dependencies between Requirements

Deshpande, Gouri; Arora, Chahal; Ruhe, Guenther

doi:10.1109/re.2019.00055

Cited by 18 publications

(17 citation statements)

References 24 publications

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“…Being able to refactor requirements enables us to deal with the, almost inevitable, likelihood that requirements will need to change [5]. This can be because of new features being added to the system, or requirements elicitation discussions identifying new requirements.…”

Section: Refactoring Requirementsmentioning

confidence: 99%

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Why just FRET when you can Refactor? Retuning FRETISH Requirements

Luckcuck¹,

Farrell²,

Sheridan³

2022

Preprint

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Formal verification of a software system relies on formalising the requirements to which it should adhere, which can be challenging. While formalising requirements from natural-language, we have dependencies that lead to duplication of information across many requirements, meaning that a change to one requirement causes updates in several places. We propose to adapt code refactorings for NASA's Formal Requirements Elicitation Tool (FRET), our tool-of-choice. Refactoring is the process of reorganising software to improve its internal structure without altering its external behaviour; it can also be applied to requirements, to make them more manageable by reducing repetition. FRET automatically translates requirements (written in its input language fretish) into Temporal Logic, which enables us to formally verify that refactoring has preserved the requirements' underlying meaning. In this paper, we present four refactorings for fretish requirements and explain their utility. We describe the application of one of these refactorings to the requirements of a civilian aircraft engine software controller, to decouple the dependencies from the duplication, and analyse how this changes the number of requirements and the number of repetitions. We evaluate our approach using Spot, a tool for checking equivalence of Temporal Logic specifications.

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Section: Refactoring Requirementsmentioning

confidence: 99%

“…Deshpande et al found that identifying dependencies between requirements is important, and that ignoring them can negatively impact a project's success [5]. They used machine learning to identify dependencies between natural-language requirements in the same set, including requires and similar relationships.…”

Section: Refactoring Requirementsmentioning

confidence: 99%

Why just FRET when you can Refactor? Retuning FRETISH Requirements

Luckcuck¹,

Farrell²,

Sheridan³

2022

Preprint

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show abstract

“…In the recent past, many empirical studies have explored diverse computational methods that used natural language processing (NLP) [10] [24], semi-supervised technique [12], hybrid techniques [11] and deep learning [18]. However, none of the approaches considered ROI to decide among techniques and the depth and breadth of their execution level.…”

Section: Empirical Analysis For Requirements Dependency Extractionmentioning

confidence: 99%

Beyond Accuracy

Deshpande

Ruhe

2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background: The unprecedented access to data has rendered a remarkable opportunity to analyze, understand, and optimize the investigation approaches in almost all the areas of (Empirical) Software Engineering. However, data analytics is time and effort consuming, thus, expensive, and not automatically valuable. Objective: This vision paper demonstrates that it is crucial to consider Return-on-Investment (ROI) when performing Data Analytics. Decisions on "How much analytics is needed"? are hard to answer. ROI could guide for decision support on the What?, How?, and How Much? analytics for a given problem. Method: The proposed conceptual framework is validated through two empirical studies that focus on requirements dependencies extraction in the Mozilla Firefox project. The two case studies are (i) Evaluation of fine-tuned BERT against Naive Bayes and Random Forest machine learners for binary dependency classification and (ii) Active Learning against passive Learning (random sampling) for REQUIRES dependency extraction. For both the cases, their analysis investment (cost) is estimated, and the achievable benefit from DA is predicted, to determine a break-even point of the investigation. Results: For the first study, fine-tuned BERT performed superior to the Random Forest, provided that more than 40% of training data is available. For the second, Active Learning achieved higher F1 accuracy within fewer iterations and higher ROI compared to Baseline (Random sampling based RF classifier). In both the studies, estimate on, How much analysis likely would pay off for the invested efforts?, was indicated by the break-even point. Conclusions: Decisions for the depth and breadth of DA of empirical data should not be made solely based on the accuracy measures. Since ROI-driven Data Analytics provides a simple yet effective direction to discover when to stop further investigation while considering the cost and value of the various types of analysis, it helps to avoid over-analyzing empirical data.

show abstract

“…In the recent past, many empirical studies have explored diverse computational methods that used natural language processing (NLP) [10] [24], semisupervised technique [11], hybrid techniques [12] and deep learning [18]. However, none of the approaches considered ROI to decide among techniques and the depth and breadth of their execution level.…”

Section: Empirical Analysis For Requirements Dependency Extractionmentioning

confidence: 99%

Beyond Accuracy: ROI-driven Data Analytics of Empirical Data

Deshpande,

Ruhe

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background: The unprecedented access to data has rendered a remarkable opportunity to analyze, understand, and optimize the investigation approaches in almost all the areas of (Empirical) Software Engineering. However, data analytics is time and effort consuming, thus, expensive, and not automatically valuable.Objective: This vision paper demonstrates that it is crucial to consider Return-on-Investment (ROI) when performing Data Analytics. Decisions on "How much analytics is needed"? are hard to answer. ROI could guide for decision support on the What?, How?, and How Much? analytics for a given problem.Method: The proposed conceptual framework is validated through two empirical studies that focus on requirements dependencies extraction in the Mozilla Firefox project. The two case studies are (i) Evaluation of fine-tuned BERT against Naive Bayes and Random Forest machine learners for binary dependency classification and (ii) Active Learning against passive Learning (random sampling) for REQUIRES dependency extraction. For both the cases, their analysis investment (cost) is estimated, and the achievable benefit from DA is predicted, to determine a break-even point of the investigation.Results: For the first study, fine-tuned BERT performed superior to the Random Forest, provided that more than 40% of training data is available. For the second, Active Learning achieved higher F1 accuracy within fewer iterations and higher ROI compared to Baseline (Random sampling based RF classifier). In both the studies, estimate on, How much analysis likely would pay off for the invested efforts?, was indicated by the break-even point.Conclusions: Decisions for the depth and breadth of DA of empirical data should not be made solely based on the accuracy measures. Since ROI-driven Data Analytics provides a simple yet effective direction to discover when to stop further investigation while considering the cost and value of the various types of analysis, it helps to avoid over-analyzing empirical data.

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Data-Driven Elicitation and Optimization of Dependencies between Requirements

Cited by 18 publications

References 24 publications

Why just FRET when you can Refactor? Retuning FRETISH Requirements

Why just FRET when you can Refactor? Retuning FRETISH Requirements

Beyond Accuracy

Beyond Accuracy: ROI-driven Data Analytics of Empirical Data

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