Canary: An Interactive and Query-Based Approach to Extract Requirements from Online Forums

Kanchev, Georgi M.; Murukannaiah, Pradeep K.; Chopra, Amit K.; Sawyer, Pete

doi:10.1109/re.2017.84

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Task type refers to the nature of the task for which crowd will participate. In Table 1, it can be seen that task type is extraction of raw requirements [34], provide feedbacks [48,39], bugs identification [21,31,20,73], feature request identification [29,52], nonfunctional requirements [31,72]. Task complexity: means whether the task is simple, medium, or complex to complete.…”

Section: Utilities In Crowdrementioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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Software systems are the joint creative products of multiple stakeholders, including both designers and users, based on their perception, knowledge and personal preferences of the application context. The rapid rise in the use of Internet, mobile and social media applications make it even more possible to provide channels to link a large pool of highly diversified and physically distributed designers and end users, the crowd. Converging the knowledge of designers and end users in requirements engineering process is essential for the success of software systems. In this paper, we report the findings of a survey of the literature on crowd-based requirements engineering research. It helps us understand the current research achievements, the areas of concentration, and how requirements related activities can be enhanced by crowd intelligence. Based on the survey, we propose a general research map and suggest the possible future roles of crowd intelligence in requirements engineering.

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Section: Utilities In Crowdrementioning

confidence: 99%

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Khan

Liu

Wen

et al. 2019

Lecture Notes in Computer Science

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show abstract

“…There has been only limited research where QA is applied for addressing RE problems. Existing works focus on requirements traceability [15]- [17], identifying compliance requirements [19], [71], and extracting information from online forums [72]. These techniques are mostly IR-based, with the exception of [19], which, like our approach, uses machine reading comprehension (MRC).…”

Section: Related Workmentioning

confidence: 99%

AI-based Question Answering Assistance for Analyzing Natural-language Requirements

Ezzini¹,

Abualhaija²,

Arora³

et al. 2023

Preprint

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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 questionanswering (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 requirementsrelated 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 domainknowledge 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%.

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“…Question-answering on requirements or other artifacts, to identify requirements, has been studied to some extent in RE. Existing work mostly focuses on querying requirements documents or other textual artifacts for retrieving information related to requirements traceability [37], [38], requirements impacted by a change [39], [40], requirements-related information from online forums [41], and requirements-related information from regulatory documents [4]. Sleimi et al [4] proposed an automated querying system using semantic web technologies for taxation laws and is the closest to our approach in the RE literature.…”

Section: Related Workmentioning

confidence: 99%

Automated Question Answering for Improved Understanding of Compliance Requirements: A Multi-Document Study

Abualhaija

Arora

Sleimi

et al. 2022

2022 IEEE 30th International Requirements Engineering Conference (RE)

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Software systems are increasingly subject to regulatory compliance. Extracting compliance requirements from regulations is challenging. Ideally, locating compliance-related information in a regulation requires a joint effort from requirements engineers and legal experts, whose availability is limited. However, regulations are typically long documents spanning hundreds of pages, containing legal jargon, applying complicated natural language structures, and including crossreferences, thus making their analysis effort-intensive. In this paper, we propose an automated questionanswering (QA) approach that assists requirements engineers in finding the legal text passages relevant to compliance requirements. Our approach utilizes largescale language models fine-tuned for QA, including BERT and three variants. We evaluate our approach on 107 question-answer pairs, manually curated by subject-matter experts, for four different European regulatory documents. Among these documents is the general data protection regulation (GDPR) -a major source for privacy-related requirements. Our empirical results show that, in ≈94% of the cases, our approach finds the text passage containing the answer to a given question among the top five passages that our approach marks as most relevant. Further, our approach successfully demarcates, in the selected passage, the right answer with an average accuracy of ≈91%.

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Canary: An Interactive and Query-Based Approach to Extract Requirements from Online Forums

Cited by 6 publications

References 2 publications

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

Crowd Intelligence in Requirements Engineering: Current Status and Future Directions

AI-based Question Answering Assistance for Analyzing Natural-language Requirements

Automated Question Answering for Improved Understanding of Compliance Requirements: A Multi-Document Study

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