Automated handling of anaphoric ambiguity in requirements

Ezzini, Saad; Abualhaija, Sallam; Arora, Chetan; Sabetzadeh, Mehrdad

doi:10.1145/3510003.3510157

Cited by 30 publications

(17 citation statements)

References 33 publications

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“…Ambiguity is a persistent issue in NL requirements. Hence, such ambiguity has been extensively studied in the literature [17,18,19,11,20]. For example, recently, Ezzini et al [20] proposed six alternative solutions for automating the handling of anaphoric ambiguity in requirements.…”

Section: Related Workmentioning

confidence: 99%

“…Hence, such ambiguity has been extensively studied in the literature [17,18,19,11,20]. For example, recently, Ezzini et al [20] proposed six alternative solutions for automating the handling of anaphoric ambiguity in requirements. These solutions incorporate both traditional and state-of-the-art NLP and ML techniques, such as Span-BERT [42].…”

Section: Related Workmentioning

confidence: 99%

“…For example, analysts sometimes describe multiple functions in a single requirement (i.e., non-atomic requirement), miss essential words (e.g., actors and verbs) or even phrases (e.g., system responses), or write a requirement following an ambiguous structure (e.g., a system response between conditions). We note that some of these quality problems have been studied individually in many prior works, such as checking the completeness [12,13,14,15,16] or ambiguity [17,18,19,11,20] of requirements. However, compared to these research strands, there has been relatively less focus on developing an automated solution that can both detect and resolve multiple quality problems in a requirement.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

On systematically building a controlled natural language for functional requirements

et al. 2021

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Natural language (NL) is pervasive in software requirements specifications (SRSs). However, despite its popularity and widespread use, NL is highly prone to quality issues such as vagueness, ambiguity, and incompleteness. Controlled natural languages (CNLs) have been proposed as a way to prevent quality problems in requirements documents, while maintaining the flexibility to write and communicate requirements in an intuitive and universally understood manner. In collaboration with an industrial partner from the financial domain, we systematically develop and evaluate a CNL, named Rimay, intended at helping analysts write functional requirements. We rely on Grounded Theory for building Rimay and follow well-known guidelines for conducting and reporting industrial case study research. Our main contributions are: (1) a qualitative methodology to systematically define a CNL for functional requirements; this methodology is intended to be general for use across information-system domains, (2) a CNL grammar to represent functional requirements; this grammar is derived from our experience in the financial domain, but should be applicable, possibly with adaptations, to other information-system domains, and (3) an empirical evaluation of our CNL (Rimay) through an industrial case study. Our contributions draw on 15 representative SRSs, collectively containing 3215 NL requirements statements from the financial domain. Our evaluation shows that Rimay is expressive enough to capture, on average, 88% (405 out of 460) of the NL requirements statements in four previously unseen SRSs from the financial domain.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On systematically building a controlled natural language for functional requirements

et al. 2021

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“…For BERT uncased, the text has been lower-cased before tokenization, whereas in BERT cased, the tokenized text is the same as the input text. Previous RE research suggests that the cased model is preferred for analyzing requirements [10,11]. We thus use the cased model in this paper.…”

Section: Introductionmentioning

confidence: 99%

Using Language Models for Enhancing the Completeness of Natural-language Requirements

Dipeeka¹,

Hassani²,

Sabetzadeh³

2023

Preprint

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Context and motivation] Incompleteness in naturallanguage requirements is a challenging problem. [Question/problem] A common technique for detecting incompleteness in requirements is checking the requirements against external sources. With the emergence of language models such as BERT, an interesting question is whether language models are useful external sources for finding potential incompleteness in requirements. [Principal ideas/results] We mask words in requirements and have BERT's masked language model (MLM) generate contextualized predictions for filling the masked slots. We simulate incompleteness by withholding content from requirements and measure BERT's ability to predict terminology that is present in the withheld content but absent in the content disclosed to BERT. [Contribution] BERT can be configured to generate multiple predictions per mask. Our first contribution is to determine how many predictions per mask is an optimal trade-off between effectively discovering omissions in requirements and the level of noise in the predictions. Our second contribution is devising a machine learning-based filter that post-processes predictions made by BERT to further reduce noise. We empirically evaluate our solution over 40 requirements specifications drawn from the PURE dataset [1]. Our results indicate that: (1) predictions made by BERT are highly effective at pinpointing terminology that is missing from requirements, and (2) our filter can substantially reduce noise from the predictions, thus making BERT a more compelling aid for improving completeness in requirements.

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“…To that end, we conducted an experiment with 25 participants simulating a requirements-dependent activity (i.e., domain modeling) using four naturallanguage requirements as input. These requirements contained two common quality defects, passive voice [32] and ambiguous pronouns [27]. Our experiment includes context factors such as experience in software engineering (SE) and RE, domain knowledge, and task experience.…”

Section: Introductionmentioning

confidence: 99%

Requirements quality research: a harmonized theory, evaluation, and roadmap

Frattini,

Montgomery,

Fischbach

et al. 2023

Requirements Eng

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High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap.

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Automated handling of anaphoric ambiguity in requirements

Cited by 30 publications

References 33 publications

On systematically building a controlled natural language for functional requirements

On systematically building a controlled natural language for functional requirements

Using Language Models for Enhancing the Completeness of Natural-language Requirements

Requirements quality research: a harmonized theory, evaluation, and roadmap

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