Does Quality of Requirements Specifications Matter? Combined Results of Two Empirical Studies

Mund, Jakob; Fernández, Daniel Méndez; Femmer, Henning; Eckhardt, Jonas

doi:10.1109/esem.2015.7321195

Cited by 14 publications

(13 citation statements)

References 19 publications

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“…This link must be carefully evaluated and analyzed in practice. Our preliminary works on this topic [23,59] provide first ideas in that direction. [13] and [53].…”

Section: Future Workmentioning

confidence: 99%

Rapid quality assurance with Requirements Smells

Femmer

Méndez

Wagner

et al. 2017

Journal of Systems and Software

Self Cite

115

102

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Context: Bad requirements quality can cause expensive consequences during the software development lifecycle, especially if iterations are long and feedback comes late. Objectives: We aim at a light-weight static requirements analysis approach that allows for rapid checks immediately when requirements are written down. Method: We transfer the concept of code smells to Requirements Engineering as Requirements Smells. To evaluate the benefits and limitations, we define Requirements Smells, realize our concepts for a smell detection in a prototype called Smella and apply Smella in a series of cases provided by three industrial and a university context. Results: The automatic detection yields an average precision of 59% at an average recall of 82% with high variation. The evaluation in practical environments indicates benefits such as an increase of the awareness of quality defects. Yet, some smells were not clearly distinguishable. Conclusion: Lightweight smell detection can uncover many practically relevant requirements defects in a reasonably precise way. Although some smells need to be defined more clearly, smell detection provides a helpful means to support quality assurance in Requirements Engineering, for instance, as a supplement to reviews.

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“…This link must be carefully evaluated and analyzed in practice. Our preliminary works on this topic [23,59] provide first ideas in that direction. [13] and [53].…”

Section: Future Workmentioning

confidence: 99%

Rapid quality assurance with Requirements Smells

Femmer

Méndez

Wagner

et al. 2017

Journal of Systems and Software

Self Cite

115

102

View full text Add to dashboard Cite

show abstract

“…To aid a correct understanding of software requirements, natural language processing techniques such as keyword extraction have been used to detect quality defects in natural language specification [10]. Experimentation often relates to factors such as the correctness and the positive or negative tone of requirements [22], and grammatical features such as passive or active voice requirements. While our approach is similar, we detect information cues 2 Treatments integrate baseline vulnerability descriptions with information provided by the standard body for CVSS.…”

Section: Background and Related Workmentioning

confidence: 99%

Identifying Relevant Information Cues for Vulnerability Assessment Using CVSS

Allodi

Bănescu

Femmer

et al. 2018

Proceedings of the Eighth ACM Conference on Data and Application Security and Privacy

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The assessment of new vulnerabilities is an activity that accounts for information from several data sources and produces a 'severity' score for the vulnerability. The Common Vulnerability Scoring System (CVSS) is the reference standard for this assessment. Yet, no guidance currently exists on which information aids a correct assessment and should therefore be considered. In this paper we address this problem by evaluating which information cues increase (or decrease) assessment accuracy. We devise a block design experiment with 67 software engineering students with varying vulnerability information and measure scoring accuracy under different information sets. We find that baseline vulnerability descriptions provided by standard vulnerability sources provide only part of the information needed to achieve an accurate vulnerability assessment. Further, we find that additional information on assets, attacks, and vulnerability type contributes in increasing the accuracy of the assessment; conversely, information on known threats misleads the assessor and decreases assessment accuracy and should be avoided when assessing vulnerabilities. These results go in the direction of formalizing the vulnerability communication to, for example, fully automate security assessments. CCS CONCEPTS• Security and privacy → Vulnerability management; • Software and its engineering → Risk management; • General and reference → Empirical studies; KEYWORDS software vulnerability assessment; vulnerability information; CVSS ACM Reference Format:

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“…Software quality maintenance correlates with the success of requirements engineering (RE) sessions [6], and the ability to efficiently analyse and select useful end-user feedback [2]. Software requirements are collected during RE sessions in the shape of pictures, flip chart notes, documentation etc.…”

Section: Introductionmentioning

confidence: 99%

Requirements-Collector: Automating Requirements Specification from Elicitation Sessions and User Feedback

Panichella

Ruiz

2020

2020 IEEE 28th International Requirements Engineering Conference (RE)

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In the context of digital transformation, speeding up the time-to-market of high-quality software products is a big challenge. Software quality correlates with the success of requirements engineering (RE) sessions and the ability to collect feedback from end-users in an efficient, dynamic way. Thus, software analysts are tasked to collect all relevant material of RE sessions and user feedback, usually specified on written notes, flip charts, pictures, and user reviews. Afterward comprehensible requirements need to be specified for software implementation and testing. These activities are mostly performed manually, which causes process delays, with a negative effect on software quality attributes such as reliability, usability, comprehensibility. This paper presents Requirements-Collector, a tool for automating the tasks of requirements specification and user feedback analysis. Our tool involves machine learning (ML) and deep learning (DL) computational mechanisms enabling the automated classification of requirements discussed in RE meetings (stored in the form of audio recordings) and textual feedback in the form of user reviews. We use such techniques as they demonstrated to be quite effective in text classification problems. We argue that Requirements-Collector has the potential to renovate the role of software analysts, which can experience a substantial reduction of manual tasks, more efficient communication, dedication to more analytical tasks, and assurance of software quality from conception phases. The results of this work have shown that our tool is able to classify RE specifications and user review feedback with reliable accuracy.

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Does Quality of Requirements Specifications Matter? Combined Results of Two Empirical Studies

Cited by 14 publications

References 19 publications

Rapid quality assurance with Requirements Smells

Rapid quality assurance with Requirements Smells

Identifying Relevant Information Cues for Vulnerability Assessment Using CVSS

Requirements-Collector: Automating Requirements Specification from Elicitation Sessions and User Feedback

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