Non-Functional Requirements Classification with Feature Extraction and Machine Learning: An Empirical Study

Haque, Md. Ariful; Rahman, Md. Abdur; Siddik, Md. Saeed

doi:10.1109/icasert.2019.8934499

Cited by 32 publications

(15 citation statements)

References 8 publications

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“…In [17], the PROMISE software engineering repository dataset was used to compare the performance of five ML algorithms (multinomial naïve Bayes (MNB), Gaussian naïve Bayes (GNB), Bernoulli naïve Bayes (BNB), K-nearest neighbor (KNN), support vector machine (SVM), stochastic gradient descent SVM (SGD SVM), and decision tree (Dtree)), along with different feature extraction techniques. Eleven categories were used to label NFRs into the categories of availability, legal, look and feel, maintainability, operational, performance, scalability, security, usability, fault tolerance, and portability.…”

Section: Classifying Nfrs Into Multi-classesmentioning

confidence: 99%

“…Then, NFRs were classified using a smaller number of classes, as used in [20], achieving a 0.91 F1 score, but NFRs were also classified into security-related or non-security-related, with a 0.77 F1 score. In [17], the authors classified NFRs into 11 classes, scoring a 76% accuracy. Only [20] provided a complete classification system.…”

Section: Comparative Analysismentioning

confidence: 99%

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One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning

Rahimi

Eassa²,

Elrefaei

2021

Entropy

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Recently, deep learning (DL) has been utilized successfully in different fields, achieving remarkable results. Thus, there is a noticeable focus on DL approaches to automate software engineering (SE) tasks such as maintenance, requirement extraction, and classification. An advanced utilization of DL is the ensemble approach, which aims to reduce error rates and learning time and improve performance. In this research, three ensemble approaches were applied: accuracy as a weight ensemble, mean ensemble, and accuracy per class as a weight ensemble with a combination of four different DL models—long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), a gated recurrent unit (GRU), and a convolutional neural network (CNN)—in order to classify the software requirement (SR) specification, the binary classification of SRs into functional requirement (FRs) or non-functional requirements (NFRs), and the multi-label classification of both FRs and NFRs into further experimental classes. The models were trained and tested on the PROMISE dataset. A one-phase classification system was developed to classify SRs directly into one of the 17 multi-classes of FRs and NFRs. In addition, a two-phase classification system was developed to classify SRs first into FRs or NFRs and to pass the output to the second phase of multi-class classification to 17 classes. The experimental results demonstrated that the proposed classification systems can lead to a competitive classification performance compared to the state-of-the-art methods. The two-phase classification system proved its robustness against the one-phase classification system, as it obtained a 95.7% accuracy in the binary classification phase and a 93.4% accuracy in the second phase of NFR and FR multi-class classification.

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Section: Classifying Nfrs Into Multi-classesmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning

Rahimi

Eassa²,

Elrefaei

2021

Entropy

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“…POS is used for tagging, and TF*IDF is used for extraction requirements. Haque et al [9] combines feature extraction with machine learning to classify Non Functional Requirements (NFR). Testing was carried out using seven machine learning algorithms with four feature selection approaches to find the best pair for performing feature extraction and classification.…”

Section: Previous Researchesmentioning

confidence: 99%

A Semantic Comparison of Feature Requirements Extraction Methods

Manek

Septiyanto

Nugroho

2021

JTS

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Requirement engineering is an essential part of software development. The initial process in software development is to determine the needs of the stakeholders. To convert stakeholder needs into features of the system to be developed takes a long time, so it is a challenge for researchers to be able to extract features automatically based on the description of the needs of stakeholders. Previous research has also implemented feature extraction using user reviews on applications that public users have used. The feature extraction results will be used for feature development in future updated versions. The extraction process can use several proven methods to provide results that match the needs of the stakeholders in the system. This study compared the automatic feature extraction method using Natural Language Processing (NLP) with Hierarchical Pattern Recognition (HPR) on the dataset requirements and user reviews. Performance evaluation was conducted to test feature extraction results using Accuracy, precision, recall, and F-measure. The study results show that each method has advantages when implemented on both datasets. The NLP method excels in classifying the NL Requirement dataset. The HPR method has its advantages in extracting user review data.

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“…In pursuance of the best evaluation of a machine learning model's performance, one can make use of the accuracy, precision, recall and F1 Score metrics -all of which make use of the number of true and false positives and negatives yielded at the end of any training method. (Haque, Rahman and Siddik, 2019). Other metrics such as negative predictive value and true negative rate can also be analyzed.…”

Section: Artificial Neural Network Designmentioning

confidence: 99%

Artificial Neural Network Solution for Anomalous Operation Detection in Power Systems

Barreto

Baer

Daros

et al. 2020

Anais Do Simpósio Brasileiro De Sistemas Elétricos 2020

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This paper presents an anomalous operation detection system for power systems using the artificial neural network approach while discussing its advantages and disadvantages. The initial data for the proposed technique is a set of simulated post-fault bus voltages and currents obtained in a sampling rate so as to emulate a phasor measurement unit network. Several types of faults are dealt with, such as three-phase to ground, two-phase, two-phase to ground and single-phase to the ground as well as line and load contingencies. All fault and steady-state simulations were performed on MATLAB using Graham Rogers’ Power System Toolbox. The artificial neural network was designed on MATLAB, using an architecture proper for pattern recognition with supervised learning and obtaining high accuracy predictions within a short amount of time. The test system used in all simulations is the IEEE 39-Bus New England Power System, which presents 10 generation units, 21 loads and three distinct areas alongside transient and sub transient models, with phasor measurement units in 14 buses. Future works are discussed, showing the possibilities for feature engineering in this type of problem, fault type detection and fault location in operation using analogous dataset and neural network structures.

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Non-Functional Requirements Classification with Feature Extraction and Machine Learning: An Empirical Study

Cited by 32 publications

References 8 publications

One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning

One- and Two-Phase Software Requirement Classification Using Ensemble Deep Learning

A Semantic Comparison of Feature Requirements Extraction Methods

Artificial Neural Network Solution for Anomalous Operation Detection in Power Systems

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