Effective use of analysts’ effort in automated tracing

Hayes, Jane Huffman; Dekhtyar, Alex; Larsen, Jody; Guéhéneuc, Yann‐Gaël

doi:10.1007/s00766-016-0260-8

Cited by 4 publications

(1 citation statement)

References 20 publications

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“…The first row of this table reveals that, in the past 10 years, there are 12 NL-Docs 38 and 11 single-item documents 5,7,39 were used in four ICSE papers. 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.…”

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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Impact of Gamification on Trace Link Vetting: A Controlled Experiment

Maro

Sundklev

Persson

et al. 2019

Requirements Engineering: Foundation for Software Quality

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Complex software systems contain a large number of interconnected development artifacts such as requirements, design models and source code. Traceability enables understanding and managing these artifacts. Establishing traceability is not a trivial task, it requires the development company to plan how traceability fits into its processes and provide tools for traceability establishment. In practice, guidelines for establishing traceability are lacking, therefore companies struggle with establishing and making the most of traceability.

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Empirical evaluation of tools for hairy requirements engineering tasks

Berry

2021

Empir Software Eng

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Effective use of analysts’ effort in automated tracing

Cited by 4 publications

References 20 publications

DRIP: Segmenting individual requirements from software requirement documents

DRIP: Segmenting individual requirements from software requirement documents

Impact of Gamification on Trace Link Vetting: A Controlled Experiment

Empirical evaluation of tools for hairy requirements engineering tasks

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