Study of Various Classifiers for Identification and Classification of Non-functional Requirements

Tóth, László; Vidács, László

doi:10.1007/978-3-319-95174-4_39

Cited by 12 publications

(14 citation statements)

References 24 publications

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“…Our work is focusing on comparison the methods including the application of the simplest deep neural network. In our former work, we have compared methods implemented in scikit-learn library [30] using the NFR dataset [38]. Our work has confirmed that the Multinomial Naive Bayes and the SVM might be the best choice if there are only a few labeled examples available.…”

Section: Introductionmentioning

confidence: 71%

“…This assumption has also been confirmed by Groen et al in their investigation [19]. To support System Analysts in choosing the appropriate procedure, investigations have been proceeded for comparison of the performance of the methods [1,9,25,38]. The researchers found that Multinomial Naive Bayes and the Support Vector Machine (SVM) using the linear kernel had given the best performance using only a few labeled examples such as the NFR dataset.…”

Section: Introductionmentioning

confidence: 73%

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Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

Tóth

Vidács

2019

ITC

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Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients rarely emphasize these specifications as much as they might be justified. Identifying, classifying and reconciling the requirements is one of the main duty of the System Analyst, which task, without using a proper tool, can be very demanding and time-consuming. Tools which support text processing are expected to improve the accuracy of identification and classification of requirements even in an unstructured set of inputs. System Analysts can use them also in document archeology tasks where many documents, regulations, standards, etc. have to be processed. Methods elaborated in natural language processing and machine learning offer a solid basis, however, their usability and the possibility to improve the performance utilizing the specific knowledge from the domain of the software engineering are to be examined thoroughly. In this paper, we present the results of our work adapting natural language processing and machine learning methods for handling and transforming textual inputs of software development. The major contribution of our work is providing a comparison of the performance and applicability of the state-of-the-art techniques used in natural language processing and machine learning in software engineering. Based on the results of our experiments, tools can be designed which can support System Analysts working on textual inputs.

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Section: Introductionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 73%

Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

Tóth

Vidács

2019

ITC

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“…A score of "0.5" means the design or validation is briefly described but lacking relevant details. A score of "0" means there is no description or it is described in such a way that it is not possible to understand the details 2014 CCHIT, WorldVista Automatic analysis [91] 2014 DePaul07 Automatic analysis [94] 2016 DePaul07, Concordia corpus Automatic analysis [74] 2016 Proprietary Automatic analysis [66] 2017 DePaul07 Automatic analysis [72] 2017 Mobile app store reviews Automatic analysis [70] 2017 PTC, MIP, CCHIT Automatic analysis [6] 2018 DePaul07 Automatic analysis [100] 2018 DePaul07 Automatic analysis [42] 2018 TAS, deletaIoT Runtime analysis [60] 2019 Mobile app store reviews Automatic analysis [10] 2019 DePaul07, Predictor models Automatic analysis [106] 2019 DePaul07, CCHIT Automatic analysis in the community. ( 2) Scale and context are key factors for applied fields such as requirements engineering, making it more challenging to design relevant experiments.…”

Section: Validationsmentioning

confidence: 99%

A systematic literature review of empirical research on quality requirements

2022

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Quality requirements deal with how well a product should perform the intended functionality, such as start-up time and learnability. Researchers argue they are important and at the same time studies indicate there are deficiencies in practice. Our goal is to review the state of evidence for quality requirements. We want to understand the empirical research on quality requirements topics as well as evaluations of quality requirements solutions. We used a hybrid method for our systematic literature review. We defined a start set based on two literature reviews combined with a keyword-based search from selected publication venues. We snowballed based on the start set. We screened 530 papers and included 84 papers in our review. Case study method is the most common (43), followed by surveys (15) and tests (13). We found no replication studies. The two most commonly studied themes are (1) differentiating characteristics of quality requirements compared to other types of requirements, (2) the importance and prevalence of quality requirements. Quality models, QUPER, and the NFR method are evaluated in several studies, with positive indications. Goal modeling is the only modeling approach evaluated. However, all studies are small scale and long-term costs and impact are not studied. We conclude that more research is needed as empirical research on quality requirements is not increasing at the same rate as software engineering research in general. We see a gap between research and practice. The solutions proposed are usually evaluated in an academic context and surveys on quality requirements in industry indicate unsystematic handling of quality requirements.

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“…There are multiple automated techniques for recognition of structure for a formal document in PDF or other formats. These techniques can be divided into two categories; machine learning techniques [13], [14] and the others are heuristics techniques [15], [16]. These methods use different ways to recognize the structure e.g.…”

Section: B Annotation Of Pdf Artifacts 1) Text Extraction From Pdf Dmentioning

confidence: 99%

“…Ameller [9], Riaz et al [1], Alkussayer et al [10], Vlas and Robinson [11], and Lee [12] used multiple techniques for NFR extractions from the text. These techniques can be categorized into two types (i) Ontological extractions [13]- [16] (ii) Text classifications or tagging [17]. We used text classification scheme for NFR extractions, different machine learning classifiers are analyzed.…”

Section: Introductionmentioning

confidence: 99%

Annotation of Software Requirements Specification (SRS), Extractions of Nonfunctional Requirements, and Measurement of Their Tradeoff

et al. 2019

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Software requirement artifacts such as manuals request for proposals, and software requirements specification (SRS) are commonly focused on functional requirements. In most SRS files, nonfunctional requirements do not formally encoded or encoded as a whole, not for an individual design problem. Moreover, these nonfunctional requirements are intermingled with functional requirements. Therefore, these nonfunctional requirements need special attention to understand for successful project development. These nonfunctional requirements have an impact on each other and optimal tradeoff is required for balanced nonfunctional requirements set. NFRs have a negative and positive tradeoff with each other such as increase confidentiality, decrease the availability, and enhance authenticity. So, an optimum tradeoff among these design problem within a module is required to have better design decisions. Instead of considering all nonfunctional requirements, the NFRs that have mutual tradeoff is considered. In this paper, we devised a novel document annotation scheme for SRS and extracted nonfunctional requirements from these annotated artifacts. In the next step, we classified NFRs into two classes security triad and performance triad, and the cost is assumed constant for each NFR. From the design problem, the tradeoff ratio is calculated among NFRs associated with it. Then, the production possibility graph is plotted to estimate the optimum tradeoff ratio within the module. For estimation economic optimum from a set of NFR, iso-cost graphs by assuming the constant cost. Some hypothetical variations in cost are also examined using 3D iso-cost graph. The reason to measure these tradeoff is to make design decision more empirical and helpful for the selection of design patterns, especially secure design patterns.

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Study of Various Classifiers for Identification and Classification of Non-functional Requirements

Cited by 12 publications

References 24 publications

Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

A systematic literature review of empirical research on quality requirements

Annotation of Software Requirements Specification (SRS), Extractions of Nonfunctional Requirements, and Measurement of Their Tradeoff

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