The linguistic approach to the natural language requirements quality: benefit of the use of an automatic tool

Fabbrini, Fabrizio; Fusani, Mario; Gnesi, Stefania; Lami, Giuseppe

doi:10.1109/sew.2001.992662

Cited by 59 publications

(67 citation statements)

References 6 publications

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“…Requirements are often error-prone due to misinterpretation of natural languages [Fabbrini et al(2001)Fabbrini, Fusani, Gnesi, and Lami]. Requirements are often characterised as complete and correct.…”

Section: Requirementsmentioning

confidence: 99%

Factors Impacting the Inputs of Traceability Recovery Approaches

Ali

Guéhéneuc

Antoniol

2011

Software and Systems Traceability

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In requirement engineering, researchers have proposed various tractability recovery approaches. To the best of our knowledge, all traceability recovery approaches have low precision and recall. Our main claim in this chapter is that there exist factors that impact the traceability approaches' inputs, in particular source document, target document, and experts' opinion, that cause low precision and recall. In this chapter, we pursue four objectives: first, to identify and document factors that impact traceability recovery approaches' inputs; second, to identify metrics/tools to measure/improve the quality of the inputs with respect to the identified factors, third, to provide precautions to control these factors, and, fourth, to empirically prove and quantify the effect of one of these factors-expert's programming knowledge-on the traceability recovery approaches' inputs. To achieve the first two objectives, we perform an incremental literature review of traceability recovery approaches and identify and document three key inputs and the seven factors impacting these inputs, out of 12 identified factors. We analyse the reported results in literature for the identified factors to address our third objective. We conduct an empirical study to assess the impact of expert's programming knowledge, to address our fourth objective. We use the effort, number of correct answers, and time to measure the effect of expert's programming knowledge on traceability recovery. We conclude that, in the literature, seven factors impacting the inputs of traceability recovery approaches have been identified, documented, and reported along with related metrics/tools and precautions. We suggest that practitioners should be wary of these seven factors and researchers should focus on the five others to improve traceability recovery approaches.

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Section: Requirementsmentioning

confidence: 99%

Factors Impacting the Inputs of Traceability Recovery Approaches

Ali

Guéhéneuc

Antoniol

2011

Software and Systems Traceability

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“…There have been several attempts and proposals to apply linguistic tools to the requirements engineering (RE) problem of identifying and eliminating ambiguity in RSs for CBSs [26,20,9,18,19]. Despite the hopes raised by the success of such tools [e.g., 12,23,13,17] in other domains, e.g., in message understanding, as evidenced by the annual Message Understanding Competition [14], these RE attempts have not been complete.…”

Section: Introductionmentioning

confidence: 99%

Requirements for tools for ambiguity identification and measurement in natural language requirements specifications

et al. 2008

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“…The use of metrics suggested in the preceding paragraph should provide for better automation of the assessment of the individual specific criteria. A number of existing tools could help this process already [9]. Moreover, prioritization of the certification properties considered should allow the assessors to gradually refine the assessment, which is especially the case in an incremental or agile software development process.…”

Section: Future Lspcmmentioning

confidence: 99%

“…Therefore, more systematic approaches should be sought. An important line of research that can be seen as complementary to our work bases requirements analysis techniques on natural language analysis techniques [9], [27], [28]. These techniques, however, do not address complexity and intricate interplay between requirements expressed using different techniques, e.g., natural language, use cases and process models.…”

Section: Related Workmentioning

confidence: 99%

Requirements Certification for Offshoring Using LSPCM

Serebrenik

Mishra

Delissen

et al. 2010

2010 Seventh International Conference on the Quality of Information and Communications Technology

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Abstract-Requirements hand-over is a common practice in software development offshoring. Cultural and geographical distance between the outsourcer and supplier, and the differences in development practices hinder the communication and lead to the misinterpretation of the original set of requirements.In this article we advocate requirements quality certification using LSPCM as a prerequisite for requirements hand-over. LSPCM stands for LaQuSo Software Product Certification Model that can be applied by non-experienced IT assessors to verify software artifacts in order to contribute to the successfulness of the project. To support our claim we have analyzed requirements of three offshoring projects using LSPCM. Application of LSPCM revealed severe flaws in one of the projects. The responsible project leader confirmed later that the development significantly exceeded time and budget. In the other project no major flaws were detected by LSPCM and it was confirmed that the implementation was delivered within time and budget.Application of LSPCM to the projects above also allowed us to refine the model for requirements hand-over in software development offshoring.

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The linguistic approach to the natural language requirements quality: benefit of the use of an automatic tool

Cited by 59 publications

References 6 publications

Factors Impacting the Inputs of Traceability Recovery Approaches

Factors Impacting the Inputs of Traceability Recovery Approaches

Requirements for tools for ambiguity identification and measurement in natural language requirements specifications

Requirements Certification for Offshoring Using LSPCM

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