A Novel Software Tool for Supporting and Automating the Requirements Engineering Process With the Use of Natural Language

Georgiades, Marinos G.; Andreou, Andreas S.

doi:10.7763/ijcte.2013.v5.727

Cited by 6 publications

(7 citation statements)

References 3 publications

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“…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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Section: Analysis Toolsmentioning

confidence: 99%

Systems performance prediction using requirements quality attributes classification

2015

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“…For the identification and development of use cases, actors, associations 2 , relationships, use case modules and use case subsystems, we utilize specific elements provided by the NLSSRE methodology [11][12][13]. NLSSRE focuses on formalizing and automating the discovery, analysis and specification of user requirements for the development of Information Systems.…”

Section: Use Case Model Developmentmentioning

confidence: 99%

“…In particular, NLSSRE provides techniques and guides for the identification of the IOs, such as an information flow table, a data flow questionnaire, IO categories (including business roles, inanimate objects, procedures, documents, events, and other animate entities) and specific rules [11,12]. A detailed description of this identification step is outside the context of this paper.…”

Section: Use Case Model Developmentmentioning

confidence: 99%

“…(8) shows a screenshot which depicts the procedure for developing the UC requirements as formalized sentences using the NALASS tool. The tool [14] automatically creates the FSUC patterns for each UC module (Fig. 8a).…”

Section: Step 4 Structure Uc Elements As Formalized Sentencesmentioning

confidence: 99%

“…Objective (i) is achieved with the use of predefined types of use cases and actors, as well as guidelines to derive their associations, relationships and business rules. The development of this formalization is guided and derived from corresponding ideas and formalization provided by the NLSSRE (Natural Language Syntax and Semantics Requirements Engineering) methodology [11][12][13][14][15] described later. The elicitation process is also facilitated by the use of formalized sentential patterns-provided by NLSSRE-which provide a structured and expressive way to write the UC elements.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Formalizing and Automating Use Case Model Development

Georgiades¹,

Andreou²

2012

TOSEJ

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This paper proposes an approach that formalizes specific elements and activities of the use case modeling process in order to overcome problematic issues common to the conventional use case methods, namely the lack of systematic elicitation support in the identification of use case elements, the vagueness introduced by the use of informal natural language to define use case specifications, and the limited support of dedicated software tools that makes UCDA a timeconsuming and error-prone activity. In particular, with the use of our approach, formalization of the stage for identifying the use case elements is achieved with the use of predefined types of use cases and actors, specific guidelines to define associations, relationships and business rules, and formalized sentential patterns. Formalization and clarity of the use case specification is achieved with the use of specific types of actions and guidelines, on one hand, and natural language-based authoring rules, on the other. A dedicated software tool supports the automation of the proposed approach including the automated generation of use case diagrams and specifications. Preliminary empirical evaluation of the proposed approach indicated its effectiveness and efficiency.

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