Automated Support to Capture Creative Requirements via Requirements Reuse

Quoc, Anh; Chekuri, Surendra Raju; Bhowmik, Tanmay

doi:10.1007/978-3-030-22888-0_4

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…Abad et al [44] compared Wizard-of-Oz (WOz) and user review analysis qualitatively. In a few studies [46,88,90], the proposed techniques have been evaluated with intended users. The rest of the studies used a case study or a proof of concept as an evaluation approach.…”

Section: Evaluation Methodsmentioning

confidence: 99%

“…Seven studies (10%) leveraged data from software repositories: the Apache OpenOffice issue tracking system [84], issues mined from the Android OS issue tracker [23], the natural language and features from the issue tracking system metadata of the four open-source projects [85], and GitHub [86], GitHub and JIRA issue tracker [87], and a data sink tank containing data from multiple software repositories (e.g., GitHub, JIRA, Jenkins, and SonarQube) [88,89] Software/app product descriptions Five studies (7%) used software product descriptions on Softpedia [90,91], app change logs [92,93], or app description page [94] Sensor readings…”

Section: Software Repositoriesmentioning

confidence: 99%

“…• K-means clustering has been used to extract software features from online reviews [41,58,63,75,78,90], and to cluster informative app reviews [53] Jiang et al [41] used S-GN to cluster online reviews, while Cleland-Huang et al in [78] used the modified Spherical K-Means to extract and cluster feature requests from threads in open discussion forums. • Kang et al [91] used the bagging clustering algorithm, which combines the EM, K-means, and MTree clustering algorithms for grouping similar data to select transfer instances.…”

Section: Traditional Clusteringmentioning

confidence: 99%

“…• Identification of candidate features has been done, using rule-based classification [40,46,61,62,100], topic modeling [49,52], traditional clustering (i.e., K-means [75] and the BIRCH algorithm [90]), a conceptual model [72,73], and rule-based clustering [74]. • In the several studies, classification was first performed, using supervised machine learning [47,48,58,60,68] or ruled-based classification [41,46] before identifying candidate software features using topic modeling [46,47,68], traditional clustering [41,58], word clouds [48], or conjoint analysis [60].…”

Section: Expression Of Final Outcomes Produced By the Automated Part mentioning

confidence: 99%

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Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

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Section: Evaluation Methodsmentioning

confidence: 99%

Section: Software Repositoriesmentioning

confidence: 99%

Section: Traditional Clusteringmentioning

confidence: 99%

Section: Expression Of Final Outcomes Produced By the Automated Part mentioning

confidence: 99%

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Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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Advances in automated support for requirements engineering: a systematic literature review

Umar,

Lano

2024

Requirements Eng

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Requirements Engineering (RE) has undergone several transitions over the years, from traditional methods to agile approaches emphasising increased automation. In many software development projects, requirements are expressed in natural language and embedded within large volumes of text documents. At the same time, RE activities aim to define software systems' functionalities and constraints. However, manually executing these tasks is time-consuming and prone to errors. Numerous research efforts have proposed tools and technologies for automating RE activities to address this challenge, which are documented in published works. This review aims to examine empirical evidence on automated RE and analyse its impact on the RE sub-domain and software development. To achieve our goal, we conducted a Systematic Literature Review (SLR) following established guidelines for conducting SLRs. We aimed to identify, aggregate, and analyse papers on automated RE published between 1996 and 2022. We outlined the output of the support tool, the RE phase covered, levels of automation, development approach, and evaluation approaches. We identified 85 papers that discussed automated RE from various perspectives and methodologies. The results of this review demonstrate the significance of automated RE for the software development community, which has the potential to shorten development cycles and reduce associated costs. The support tools primarily assist in generating UML models (44.7%) and other activities such as omission of steps, consistency checking, and requirement validation. The analysis phase of RE is the most widely automated phase, with 49.53% of automated tools developed for this purpose. Natural language processing technologies, particularly POS tagging and Parser, are widely employed in developing these support tools. Controlled experimental methods are the most frequently used (48.2%) for evaluating automated RE tools, while user studies are the least employed evaluation method (8.2%). This paper contributes to the existing body of knowledge by providing an updated overview of the research literature, enabling a better understanding of trends and state-of-the-art practices in automated RE for researchers and practitioners. It also paves the way for future research directions in automated requirements engineering.

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Α tool for requirements engineering using ontologies and boilerplates

Antοniou,

Bassiliades

2023

Autom Softw Eng

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The most popular technique for specification requirements is natural language. The disadvantage of natural language is ambiguity. Boilerplates are syntactic patterns which limit the ambiguity problem associated with using natural language to specify system/software requirements. Also, using boilerplates is considered a useful tool for inexperienced engineers to define requirements. Using linguistic boilerplates, constrains the natural language syntactically. Furthermore, a domain-specific ontology is used to constrain requirements semantically, as well. In requirements specification, using ontologies helps to restrict the vocabulary to entities, properties, and property relationships which are semantically related. The above results in avoiding or making fewer mistakes. This work makes use of the combination of boilerplate and ontology. Usually, the attributes of boilerplates are completed with the help of the ontology. The contribution of this paper is that the whole boilerplates is stored in the ontology and attributes and fixed elements are part of the ontology. This combination helps to correct semantically and syntactically requirement construction. This paper proposes a tool based on a domain-specific ontology and a set of predefined generic linguistic boilerplates for requirements engineering. We create a domain-specific ontology and a minimal set of boilerplates for an ATM (Automated Teller Machine). We carried out an experiment in order to obtain evidence for the effectiveness and efficiency of our method. The experiment took the form of a case study for the ATM domain and our proposed method was evaluated by users. The contribution and novelty of our methodology is that we created a tool for defining requirements that integrates boilerplate templates and an ontology. We exploit the boilerplate language syntax, mapping them to Resource Description Framework triples which have also a linguistic nature.

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Automated Support to Capture Creative Requirements via Requirements Reuse

Cited by 5 publications

References 27 publications

Data-Driven Requirements Elicitation: A Systematic Literature Review

Data-Driven Requirements Elicitation: A Systematic Literature Review

Advances in automated support for requirements engineering: a systematic literature review

Α tool for requirements engineering using ontologies and boilerplates

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