RETRACTED ARTICLE: An integrated approach towards automated software requirements elicitation from unstructured documents

Murugesh, S.; Jaya, A.

doi:10.1007/s12652-019-01667-7

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Alsolai et al [55] conducted an empirical evaluation to find the impact of feature selection techniques, ensemble models, and sampling techniques implemented on seven data sets for predicting change-proneness using different source code metrics. However, Murugesh et al [56] discussed automated software requirements using machine learning algorithms.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

et al. 2022

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Background: Refactoring is changing a software system without affecting the software functionality. The current researchers aim i to identify the appropriate method(s) or class(s) that needs to be refactored in object-oriented software. Ensemble learning helps to reduce prediction errors by amalgamating different classifiers and their respective performances over the original feature data. Other motives are added in this paper regarding several ensemble learners, errors, sampling techniques, and feature selection techniques for refactoring prediction at the class level. Objective: This work aims to develop an ensemble-based refactoring prediction model with structural identification of source code metrics using different feature selection techniques and data sampling techniques to distribute the data uniformly. Our model finds the best classifier after achieving fewer errors during refactoring prediction at the class level. Methodology: At first, our proposed model extracts a total of 125 software metrics computed from object-oriented software systems processed for a robust multi-phased feature selection method encompassing Wilcoxon significant text, Pearson correlation test, and principal component analysis (PCA). The proposed multi-phased feature selection method retains the optimal features characterizing inheritance, size, coupling, cohesion, and complexity. After obtaining the optimal set of software metrics, a novel heterogeneous ensemble classifier is developed using techniques such as ANN-Gradient Descent, ANN-Levenberg Marquardt, ANN-GDX, ANN-Radial Basis Function; support vector machine with different kernel functions such as LSSVM-Linear, LSSVM-Polynomial, LSSVM-RBF, Decision Tree algorithm, Logistic Regression algorithm and extreme learning machine (ELM) model are used as the base classifier. In our paper, we have calculated four different errors i.e., Mean Absolute Error (MAE), Mean magnitude of Relative Error (MORE), Root Mean Square Error (RMSE), and Standard Error of Mean (SEM). Result: In our proposed model, the maximum voting ensemble (MVE) achieves better accuracy, recall, precision, and F-measure values (99.76, 99.93, 98.96, 98.44) as compared to the base trained ensemble (BTE) and it experiences less errors (MAE = 0.0057, MORE = 0.0701, RMSE = 0.0068, and SEM = 0.0107) during its implementation to develop the refactoring model. Conclusions: Our experimental result recommends that MVE with upsampling can be implemented to improve the performance of the refactoring prediction model at the class level. Furthermore, the performance of our model with different data sampling techniques and feature selection techniques has been shown in the form boxplot diagram of accuracy, F-measure, precision, recall, and area under the curve (AUC) parameters.

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Section: Logistic Regression (Lr)mentioning

confidence: 99%

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

et al. 2022

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“…At present, the preparation methods of requirements specification documents mainly include the manual preparation of Unified Modeling Language (UML), generation of requirements specifications based on extraction rules [5,6] and automatic generation of requirements specifications based on natural language processing [7]. Although manual preparation has a strong personalized customization ability to generate demand specifications, it is usually difficult to solve the problems of cumbersome manual preparation, being error prone and difficulty communicating effectively with business personnel, which are easily affected by the subjective ability of the writer [8].…”

Section: Introductionmentioning

confidence: 99%

BiLSTM-CRF-KG: A Construction Method of Software Requirements Specification Graph

Wang

Pan

Chen³

et al. 2022

Applied Sciences

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A requirement analysis is the basis and source of software system development, and its accuracy, consistency and completeness are the keys to determining software quality. However, at present, most software requirements specifications are prepared manually, which has some problems, such as inconsistency with business description, low preparation efficiency, being error prone and difficulty communicating effectively with business personnel. Aiming at the above problems, this paper realizes a construction model of the software requirements specification graph BiSLTM-CRF-KG by using natural language processing and knowledge graph technology. Simulation experiments on 150 real software system business requirements description corpora show that the BiSLTM-CRF-KG model can obtain 96.31% functional entity recognition accuracy directly from the original corpus, which is better than the classical BiSLTM-CRF, IDCNN-CRF and CRF++ models, and has good performance on different kinds of data sets.

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A hybrid technique using minimal spanning tree and analytic hierarchical process to prioritize functional requirements for parallel software development

2023

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RETRACTED ARTICLE: An integrated approach towards automated software requirements elicitation from unstructured documents

Cited by 4 publications

References 8 publications

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

Class-Level Refactoring Prediction by Ensemble Learning with Various Feature Selection Techniques

BiLSTM-CRF-KG: A Construction Method of Software Requirements Specification Graph

A hybrid technique using minimal spanning tree and analytic hierarchical process to prioritize functional requirements for parallel software development

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