A Feasibility Study of Automated Natural Language Requirements Analysis in Market-Driven Development

Dag, Johan Natt och; Regnell, Björn; Carlshamre, Pär; Andersson, Michael; Karlsson, Jenny

doi:10.1007/s007660200002

Cited by 82 publications

(39 citation statements)

References 24 publications

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“…Between documentation and code [13,117] between requirements and requirements [82,125,126] between requirements and testcases [46,112,113] Between requirements and model elements [40,46,112,113] None so far…”

Section: Based On Information-retrievalmentioning

confidence: 99%

“…In an early approach to link identification using similarity measures, Natt och Dag et al [126] have investigated the automatic detection of requirements relations and similarity by directly applying similarity measures to sentences based on common words. They conclude that their techniques, and fully automated processing of natural language in general, cannot replace human judgment.…”

Section: Approaches Based On Information Retrievalmentioning

confidence: 99%

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A survey of traceability in requirements engineering and model-driven development

2009

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Traceability-the ability to follow the life of software artifacts-is a topic of great interest to software developers in general, and to requirements engineers and model-driven developers in particular. This article aims to bring those stakeholders together by providing an overview of the current state of traceability research and practice in both areas. As part of an extensive literature survey, we identify commonalities and differences in these areas and uncover several unresolved challenges which affect both domains. A good common foundation for further advances regarding these challenges appears to be a combination of the formal basis and the automated recording opportunities of MDD on the one hand, and the more holistic view of traceability in the requirements engineering domain on the other hand.Keywords Requirements engineering · Model-driven engineering · Model-driven development · Traceability

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Section: Based On Information-retrievalmentioning

confidence: 99%

Section: Approaches Based On Information Retrievalmentioning

confidence: 99%

A survey of traceability in requirements engineering and model-driven development

2009

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“…Nagelkerke R 2 -this descriptor has a value from zero to one and indicates the amount of variation in the dependent variable that is explained by the logistic model [28]. A number closer to one indicates a strong relationship between the predictors and the prediction.…”

Section: Binary Logistic Regressionmentioning

confidence: 99%

“…Requirements analysis can be accomplished through expert judgment, techniques such as case-based reasoning [27], or use of automated analysis tools [28,29]. According to a recent empirical study completed by Lami [7], automated requirements analysis tools can be more effective than human reviews for finding defective requirements.…”

Section: Analysis Toolsmentioning

confidence: 99%

Systems performance prediction using requirements quality attributes classification

2015

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Poor requirements definition can adversely impact system cost and performance for government acquisition programs. This can be mitigated by ensuring requirements statements are written in a clear and unambiguous manner with high linguistic quality. This paper introduces a statistical model that uses requirements quality factors to predict system operational performance. This work explores four classification techniques (Logistic Regression, Naïve Bayes Classifier, Support Vector Machine, and K-Nearest Neighbor) to develop the predictive model. This model is created using empirical data from current major acquisition programs within the federal government. Operational Requirements Documents and Operational Test Reports are the data sources, respectively, for the system requirements statements and the accompanying operational test results used for model development.A commercial-off-the-shelf requirements quality analysis tool is used to determine the requirements linguistic quality metrics used in the model. Subsequent to model construction, the predictive value of the model is confirmed through execution of a sensitivity analysis, cross-validation of the data, and an overfitting analysis. Lastly, Receiver Operating Characteristics are examined to determine the best performing model. In all, the results establish that requirements quality is indeed a predictive factor for endsystem operational performance, and the resulting statistical model can influence requirements development based on likelihood of successful operational performance.

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“…Many natural language processing techniques have been studied to support automatic requirements analysis [18][19][20][21][22]24]. These techniques have used various methods including, but not limited to: sliding windows, similarity measurement, and semantic networks for requirements analysis.…”

Section: Introductionmentioning

confidence: 99%

Using classification techniques for informal requirements in the requirements analysis-supporting system

Park

Seo

et al. 2007

Information and Software Technology

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In order to efficiently develop large-scale and complicated software, it is important for system engineers to correctly understand users' requirements. Most requirements in large-scale projects are collected from various stakeholders located in various regions, and they are generally written in natural language. Therefore, the initial collected requirements must be classified into various topics prior to analysis phases in order to be usable as input in several requirements analysis methods. If this classification process is manually done by analysts, it becomes a time-consuming task. To solve this problem, we propose a new bootstrapping method which can automatically classify requirements sentences into each topic category using only topic words as the representative of the analysts' views. The proposed method is verified through experiments using two requirements data sets: one written in English and the other in Korean. The significant performances were achieved in the experiments: the 84.28 and 87.91 F1 scores for the English and Korean data sets, respectively. As a result, the proposed method can provide an effective function for an Internet-based requirements analysis-supporting system so as to efficiently gather and analyze requirements from various and distributed stakeholders by using the Internet.

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A Feasibility Study of Automated Natural Language Requirements Analysis in Market-Driven Development

Cited by 82 publications

References 24 publications

A survey of traceability in requirements engineering and model-driven development

A survey of traceability in requirements engineering and model-driven development

Systems performance prediction using requirements quality attributes classification

Using classification techniques for informal requirements in the requirements analysis-supporting system

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