Toward practical adoption of i* framework: an automatic two-level layout approach

Wang, Yunduo; Li, Tong; Zhou, Qixiang; Du, Jinhui

doi:10.1007/s00766-021-00346-4

Cited by 7 publications

(4 citation statements)

References 28 publications

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“…According to the second row, there are two NL-Docs, 40,41 six single-item documents 5,42,43 and four models-based requirement sets 42,[44][45][46] were used in RE papers. The third row shows that there are seven NL-Docs, 47,48 seven single-item documents 40,[48][49][50][51][52][53] and fives models documents [54][55][56] were used in REJ papers. For the REFSQ papers, 75 NL-Doc, 13,57,58 one single-item documents 59 and one model 60 are involved.…”

Section: Survey On the Distribution Of Requirements Adhesionmentioning

confidence: 99%

DRIP: Segmenting individual requirements from software requirement documents

Zhao,

Zhang,

Lian

et al. 2023

Softw Pract Exp

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Numerous academic research projects and industrial tasks related to software engineering require individual requirements as input. Unfortunately, according to our observation, several requirements may be packed in one paragraph without explicit boundaries in specification documents. To understand this problem's prevalence, we performed a preliminary study on the open requirement documents widely used in the academic community over the last 10 years, and found that 26% of them include this phenomenon. Several text segmentation approaches have been reported; however, they tend to identify topically coherent units which may contain more than one requirement. What is more, they do not take the constitutions of semantic units of requirements into consideration. Here we report a two‐phase learning‐based approach named DRIP to segment individual requirements from paragraphs. To be specific, we first propose a Requirement Segmentation Siamese framework, which models the similarity of sentences and their conjunction relations, and then detects the initial boundaries between individual requirements. Then, we optimize the boundaries heuristically based on the semantic completeness validation of the segments. Experiments with 1132 paragraphs and 6826 sentences show that DRIP outperforms the popular unsupervised and supervised text segmentation algorithms with respect to processing different documents (with accuracy gains of 57.65%–187.53%) and processing paragraphs of different complexity (with average accuracy gains of 54.46%–158.68%). We also show the importance of each component of DRIP to the segmentation.

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Section: Survey On the Distribution Of Requirements Adhesionmentioning

confidence: 99%

DRIP: Segmenting individual requirements from software requirement documents

Zhao,

Zhang,

Lian

et al. 2023

Softw Pract Exp

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“…Feature model [18], [19]; Pattern-based model [20], [21]; MAPE-K model [22], [23]; Agent / multiagent-based model [24] - [28]; Viewpoint model [29] , [30]; Problem-Based model [31], [32]; Continues Collaborative Model (CCM) [33]; Integrated-Secure SDLC model (IS-SDLC) [34]; Requirements frame model [35]; Grid Model [36]; Probabilistic model checking [37]; BIP (Behavior -Interaction -Priorities) Model [38]; [29], [39] model based testing; [40] model driven approach (MDA); [20] Model Driven Development (MDD); BERT Model [41] - [43]; RE-BERT Model [44]; Object-Role Modelling (ORM) notation [45]; i* model [46], [47]; scenario based modeling [48]; QR (Quality Requirements) Pattern [49] Annotated BPMN Model [50] Specific Method…”

Section: Model-based Approachmentioning

confidence: 99%

Automatic Requirements Engineering: Activities, Methods, Tools, and Domains – A Systematic Literature Review

Delima

Mustofa

Sari

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Requirements engineering (RE) is an initial activity in the software engineering process that involves many users. The involvement of various users in the RE process raises ambiguity and vagueness in requirements modeling. In addition, traditional RE is a time-consuming activity. Therefore various studies have been conducted to support process automation on RE. This paper conducts a systematic literature review (SLR) to obtain information about RE automation related to RE activities, methods/models, tools, and domains. SLR is done through 5 main stages: definition of research questions, conducting the search, screening for relevant papers, data extraction, mapping, and analysis. The data extraction and mapping are carried out on 155 relevant publications from 2016 to 2022. Based on the results from SLR, around 53% of the research focuses on RE automation in analysis and specifications, 40% focuses on elicitation, validation, and requirements management, and 7% focuses on requirements quality. NLP is the most used method in elicitation and specification, while for analysis, machine learning, NLP, and goal-oriented models are mostly used in automatic RE. Furthermore, many papers use specific models and methods for validation and requirements management. From the domain analysis results, it is obtained that more than half of the papers contribute directly to the RE domain, and some contribute to the development of RE automation in the software application domain.

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“…Previous researches proved the relevance and utility of i* to model organizational requirements of a “multi-agent system” [44] facilitating stakeholder’s interactions by depicting their dependencies and hence providing a mean for coordination. i* was previously used to model several organizational settings [57] like online stores [58] , hospital beds management and health care [6] , [53] , supply chains and more specifically outbound logistics [59] , production support in the steel industry [60] and also for the development of higher education platforms like collaborative learning software [48] and Massive Open Online Courses (MOOCs) [61] . The i* framework is divided in two parts, each providing a different level of abstraction: the Strategic Dependency (SD) model (used for the representation of actor-interdependencies) and the Strategic Rationale (SR) model (used for the representation of the actors’ internal intents) [34] .…”

Section: Model-based Value Representationmentioning

confidence: 99%