Software feature extraction using infrequent feature extraction

Putri, Divi Galih Prasetyo; Siahaan, Daniel

doi:10.1109/inaes.2016.7821927

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…So from these data, feature extraction can be carried out to be used as a reference for future feature improvements. Putri and Siahaan [10] made improvements in extracting less frequently mentioned software features based on end-user review. To enhanced the result produced by the collocation finding methods, the etymological rules were added.…”

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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Section: Previous Researchesmentioning

confidence: 99%

A Semantic Comparison of Feature Requirements Extraction Methods

Manek

Septiyanto

Nugroho

2021

JTS

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“…Aiming at handling abnormal sound detection problems in the early stages [5], Koizumi et al [6] proposed using the Gaussian mixture model to calculate anomaly scores [7], and Foggia et al [5] used audio streams to perform sound detection to determine dangerous situations. However, traditional algorithms cannot handle high-dimensional data, and feature extraction capabilities are weak.…”

Section: Introductionmentioning

confidence: 99%

Research on the enhancement of machine fault evaluation model based on data-driven

Cui

Luo

et al. 2022

Int. J. Metrol. Qual. Eng.

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Recently fault data diagnosis-based deep learning methods have achieved promising results. However, most of these methods' performances are difficult to improve once they have achieved accuracy. This paper mainly uses fusion theory based on data-driven to solve this problem. Firstly, the diagnostic models are divided into feature extraction and neural network. Then, four feature extraction methods are fused by pre-allocation. The neural network part consists of three single models, and the weight of the three output results is determined by regression analysis. Experiments show that the accuracy of diagnostic models is improved. Finally, we combine the two studies and propose a Fusion-Ensemble superposition (FES) model. The AUC value of the model is higher than 98% in most tasks of the DCASE2020 machine failure dataset.

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“…The reuse of these requirements can reduce costs in the elicitation process [14]. Requirements reuse could be from formal [15] or informal sources [16], product description sources [17], user reviews [18][19][20], expert reviews [18], social media [21,22], and online news [5,23].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Software Feature Extraction Results Using Sentiment Analysis to Aid Requirements Reuse

et al. 2021

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Recently, feature extraction from user reviews has been used for requirements reuse to improve the software development process. However, research has yet to use sentiment analysis in the extraction for it to be well understood. The aim of this study is to improve software feature extraction results by using sentiment analysis. Our study’s novelty focuses on the correlation between feature extraction from user reviews and results of sentiment analysis for requirement reuse. This study can inform system analysis in the requirements elicitation process. Our proposal uses user reviews for the software feature extraction and incorporates sentiment analysis and similarity measures in the process. Experimental results show that the extracted features used to expand existing requirements may come from positive and negative sentiments. However, extracted features with positive sentiment overall have better values than negative sentiments, namely 90% compared to 63% for the relevance value, 74–47% for prompting new features, and 55–26% for verbatim reuse as new requirements.

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Software feature extraction using infrequent feature extraction

Cited by 7 publications

References 7 publications

A Semantic Comparison of Feature Requirements Extraction Methods

A Semantic Comparison of Feature Requirements Extraction Methods

Research on the enhancement of machine fault evaluation model based on data-driven

Enhancing Software Feature Extraction Results Using Sentiment Analysis to Aid Requirements Reuse

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