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See detailAI-based Question Answering Assistance for Analyzing Natural-language Requirements
Ezzini, Saad UL; Abualhaija, Sallam UL; Arora, Chetan UL et al

in In Proceedings of the 45th International Conference on Software Engineering (ICSE'23), Melbourne 14-20 May 2023 (in press)

Abstract—By virtue of being prevalently written in natural language (NL), requirements are prone to various defects, e.g., inconsistency and incompleteness. As such, requirements are frequently subject to ... [more ▼]

Abstract—By virtue of being prevalently written in natural language (NL), requirements are prone to various defects, e.g., inconsistency and incompleteness. As such, requirements are frequently subject to quality assurance processes. These processes, when carried out entirely manually, are tedious and may further overlook important quality issues due to time and budget pressures. In this paper, we propose QAssist – a question-answering (QA) approach that provides automated assistance to stakeholders, including requirements engineers, during the analysis of NL requirements. Posing a question and getting an instant answer is beneficial in various quality-assurance scenarios, e.g., incompleteness detection. Answering requirements-related questions automatically is challenging since the scope of the search for answers can go beyond the given requirements specification. To that end, QAssist provides support for mining external domain-knowledge resources. Our work is one of the first initiatives to bring together QA and external domain knowledge for addressing requirements engineering challenges. We evaluate QAssist on a dataset covering three application domains and containing a total of 387 question-answer pairs. We experiment with state-of-the-art QA methods, based primarily on recent large-scale language models. In our empirical study, QAssist localizes the answer to a question to three passages within the requirements specification and within the external domain-knowledge resource with an average recall of 90.1% and 96.5%, respectively. QAssist extracts the actual answer to the posed question with an average accuracy of 84.2%. Index Terms—Natural-language Requirements, Question Answering (QA), Language Models, Natural Language Processing (NLP), Natural Language Generation (NLG), BERT, T5. [less ▲]

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See detailArtificial Intelligence-enabled Automation For Ambiguity Handling And Question Answering In Natural-language Requirements
Ezzini, Saad UL

Doctoral thesis (2022)

Requirements Engineering (RE) quality control is a crucial step for a project’s success. Natural Language (NL) is by far the most commonly used means for capturing requirement specifications. Despite ... [more ▼]

Requirements Engineering (RE) quality control is a crucial step for a project’s success. Natural Language (NL) is by far the most commonly used means for capturing requirement specifications. Despite facilitating communication, NL is prone to quality defects, one of the most notable of which is ambiguity. Ambiguous requirements can lead to misunderstandings and eventually result in a system that is different from what is intended, thus wasting time, money, and effort in the process. This dissertation tackles selected quality issues in NL requirements: • Using Domain-specific Corpora for Improved Handling of Ambiguity in Requirements: Syntactic ambiguity types occurring in coordination and prepositional-phrase attachment structures are prevalent in requirements (in our document collection, as we discuss in Chapter 3, 21% and 26% of the requirements are subject to coordination and prepositional-phrase attachment ambiguity analysis, respectively). We devise an automated solution based on heuristics and patterns for improved handling of coordination and prepositional-phrase attachment ambiguity in requirements. As a prerequisite for this research, we further develop a more broadly applicable corpus generator that creates a domain-specific knowledge resource by crawling Wikipedia. • Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study: Anaphoric ambiguity is another prevalent ambiguity type in requirements. Estimates from the RE literature suggest that nearly 20% of industrial requirements contain anaphora [1, 2]. We conducted a multi-solution study for anaphoric ambiguity handling. Our study investigates six alternative solutions based on three different technologies: (i) off-the-shelf natural language processing (NLP), (ii) recent NLP methods utilizing language models, and (iii) machine learning (ML). • AI-based Question Answering Assistant for Analyzing NL Requirements: Understanding NL requirements requires domain knowledge that is not necessarily shared by all the involved stakeholders. We develop an automated question-answering assistant that supports requirements engineers during requirements inspections and quality assurance. Our solution uses advanced information retrieval techniques and machine reading comprehension models to answer questions from the same requirement specifications document and/or an external domain-specific knowledge resource. All the research components in this dissertation are tool-supported. Our tools are released with open-source licenses to encourage replication and reuse. [less ▲]

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See detailAutomated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study
Ezzini, Saad UL; Abualhaija, Sallam UL; Arora, Chetan et al

in In Proceedings of the 44th International Conference on Software Engineering (ICSE'22), Pittsburgh, PA, USA 22-27 May 2022 (2022, May)

Ambiguity is a pervasive issue in natural-language requirements. A common source of ambiguity in requirements is when a pronoun is anaphoric. In requirements engineering, anaphoric ambiguity occurs when a ... [more ▼]

Ambiguity is a pervasive issue in natural-language requirements. A common source of ambiguity in requirements is when a pronoun is anaphoric. In requirements engineering, anaphoric ambiguity occurs when a pronoun can plausibly refer to different entities and thus be interpreted differently by different readers. In this paper, we develop an accurate and practical automated approach for handling anaphoric ambiguity in requirements, addressing both ambiguity detection and anaphora interpretation. In view of the multiple competing natural language processing (NLP) and machine learning (ML) technologies that one can utilize, we simultaneously pursue six alternative solutions, empirically assessing each using a collection of ~1,350 industrial requirements. The alternative solution strategies that we consider are natural choices induced by the existing technologies; these choices frequently arise in other automation tasks involving natural-language requirements. A side-by-side empirical examination of these choices helps develop insights about the usefulness of different state-of-the-art NLP and ML technologies for addressing requirements engineering problems. For the ambiguity detection task, we observe that supervised ML outperforms both a large-scale language model, SpanBERT (a variant of BERT), as well as a solution assembled from off-the-shelf NLP coreference resolvers. In contrast, for anaphora interpretation, SpanBERT yields the most accurate solution. In our evaluation, (1) the best solution for anaphoric ambiguity detection has an average precision of ~60% and a recall of 100%, and (2) the best solution for anaphora interpretation (resolution) has an average success rate of ~98%. [less ▲]

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See detailTAPHSIR: Towards AnaPHoric Ambiguity Detection and ReSolution In Requirements
Ezzini, Saad UL; Abualhaija, Sallam UL; Arora, Chetan et al

in ACM SIGSOFT CONFERENCE ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (2022)

We introduce TAPHSIR -- a tool for anaphoric ambiguity detection and anaphora resolution in requirements. TAPHSIR facilities reviewing the use of pronouns in a requirements specification and revising ... [more ▼]

We introduce TAPHSIR -- a tool for anaphoric ambiguity detection and anaphora resolution in requirements. TAPHSIR facilities reviewing the use of pronouns in a requirements specification and revising those pronouns that can lead to misunderstandings during the development process. To this end, TAPHSIR detects the requirements which have potential anaphoric ambiguity and further attempts interpreting anaphora occurrences automatically. TAPHSIR employs a hybrid solution composed of an ambiguity detection solution based on machine learning and an anaphora resolution solution based on a variant of the BERT language model. Given a requirements specification, TAPHSIR decides for each pronoun occurrence in the specification whether the pronoun is ambiguous or unambiguous, and further provides an automatic interpretation for the pronoun. The output generated by TAPHSIR can be easily reviewed and validated by requirements engineers. TAPHSIR is publicly available on Zenodo (https://doi.org/10.5281/zenodo.5902117). [less ▲]

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See detailWikiDoMiner: Wikipedia Domain-Specific Miner
Ezzini, Saad UL; Abualhaija, Sallam UL; Sabetzadeh, Mehrdad

in ACM SIGSOFT CONFERENCE ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (2022)

We introduce WikiDoMiner -- a tool for automatically generating domain-specific corpora by crawling Wikipedia. WikiDoMiner helps requirements engineers create an external knowledge resource that is ... [more ▼]

We introduce WikiDoMiner -- a tool for automatically generating domain-specific corpora by crawling Wikipedia. WikiDoMiner helps requirements engineers create an external knowledge resource that is specific to the underlying domain of a given requirements specification (RS). Being able to build such a resource is important since domain-specific datasets are scarce. WikiDoMiner generates a corpus by first extracting a set of domain-specific keywords from a given RS, and then querying Wikipedia for these keywords. The output of WikiDoMiner is a set of Wikipedia articles relevant to the domain of the input RS. Mining Wikipedia for domain-specific knowledge can be beneficial for multiple requirements engineering tasks, e.g., ambiguity handling, requirements classification, and question answering. WikiDoMiner is publicly available on Zenodo under an open-source license (https://doi.org/10.5281/zenodo.6672682) [less ▲]

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See detailUsing Domain-specific Corpora for Improved Handling of Ambiguity in Requirements
Ezzini, Saad UL; Abualhaija, Sallam UL; Arora, Chetan et al

in In Proceedings of the 43rd International Conference on Software Engineering (ICSE'21), Madrid 25-28 May 2021 (2021, May)

Ambiguity in natural-language requirements is a pervasive issue that has been studied by the requirements engineering community for more than two decades. A fully manual approach for addressing ambiguity ... [more ▼]

Ambiguity in natural-language requirements is a pervasive issue that has been studied by the requirements engineering community for more than two decades. A fully manual approach for addressing ambiguity in requirements is tedious and time-consuming, and may further overlook unacknowledged ambiguity – the situation where different stakeholders perceive a requirement as unambiguous but, in reality, interpret the requirement differently. In this paper, we propose an automated approach that uses natural language processing for handling ambiguity in requirements. Our approach is based on the automatic generation of a domain-specific corpus from Wikipedia. Integrating domain knowledge, as we show in our evaluation, leads to a significant positive improvement in the accuracy of ambiguity detection and interpretation. We scope our work to coordination ambiguity (CA) and prepositional-phrase attachment ambiguity (PAA) because of the prevalence of these types of ambiguity in natural-language requirements [1]. We evaluate our approach on 20 industrial requirements documents. These documents collectively contain more than 5000 requirements from seven distinct application domains. Over this dataset, our approach detects CA and PAA with an average precision of 80% and an average recall of 89% (90% for cases of unacknowledged ambiguity). The automatic interpretations that our approach yields have an average accuracy of 85%. Compared to baselines that use generic corpora, our approach, which uses domain-specific corpora, has 33% better accuracy in ambiguity detection and 16% better accuracy in interpretation. [less ▲]

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See detailMAANA: An Automated Tool for DoMAin-specific HANdling of Ambiguity
Ezzini, Saad UL; Abualhaija, Sallam UL; Arora, Chetan et al

in Companion Proceedings of the 43rd International Conference on Software Engineering (2021, May)

MAANA (in Arabic: “meaning”) is a tool for performing domain-specific handling of ambiguity in requirements. Given a requirements document as input, MAANA detects the requirements that are potentially ... [more ▼]

MAANA (in Arabic: “meaning”) is a tool for performing domain-specific handling of ambiguity in requirements. Given a requirements document as input, MAANA detects the requirements that are potentially ambiguous. The focus of MAANA is on coordination ambiguity and prepositional-phrase attachment ambiguity; these are two common ambiguity types that have been studied in the requirements engineering literature. To detect ambiguity, MAANA utilizes structural patterns and a set of heuristics derived from a domain-specific corpus. The generated analysis file after running the tool can be reviewed by requirements analysts. Through combining different knowledge sources, MAANA highlights also the requirements that might contain unacknowledged ambiguity. That is when the analysts understand different interpretations for the same requirement, without explicitly discussing it with the other analysts due to time constraints. This artifact paper presents the details of MAANA. MAANA is associated with the ICSE 2021 technical paper titled “Using Domain-specific Corpora for Improved Handling of Ambiguity in Requirements”. The tool is publicly available on GitHub and Zenodo. [less ▲]

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