An Empirical Study to Investigate Different SMOTE Data Sampling Techniques for Improving Software Refactoring Prediction

Panigrahi, Rasmita; Kumar, Lov; Kuanar, Sanjay K.

doi:10.1007/978-3-030-63820-7_3

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…So, the balancing of data is required to improve the predictive ability of the developed Sentiment Analysis Models. We have performed Synthetic Minority Oversampling Technique (SMOTE) and Borderline Synthetic Minority Oversampling Technique (Borderline-SMOTE) on each dataset to balance the data [10].…”

Section: A Experimental Datasetmentioning

confidence: 99%

Software Sentiment Analysis using Deep-learning Approach with Word-Embedding Techniques

Mula¹,

Kumar²,

Murthy³

et al. 2022

Annals of Computer Science and Information Systems

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Sentiment Analysis in the Software Engineering community aims to make the development and maintenance of software a better experience by helping provide code and library suggestions, defect-related comments for source code, etc. The manual finding of sentiment-based comments may be an inaccurate prediction and a time-consuming process. Automating the sentiment analysis process by leveraging Machine Learning models can benefit software professionals by giving them insights into other developers and feelings about software products, libraries, development, and maintenance tasks at a glance. This study aims to develop software sentiment prediction models based on comments by (1) identifying the best embedding techniques to represent the word of the comments, not just as a number but as a vector in n-dimensional space (2) finding the best sets of vectors using different features selection techniques (3) finding the best methods to handle the class imbalance nature of the data, and (4) finding the best architecture of deep-learning for the training of models. The developed models are validated using 5fold cross-validation with four different performance parameters: accuracy, AUC, recall, and precision on three different datasets. The experimental finding shows that the models developed using the word embeddings with feature selection using Deep Learning classifiers on balanced data can significantly predict the underlying sentiments of textual comments.

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Section: A Experimental Datasetmentioning

confidence: 99%

Software Sentiment Analysis using Deep-learning Approach with Word-Embedding Techniques

Mula¹,

Kumar²,

Murthy³

et al. 2022

Annals of Computer Science and Information Systems

Self Cite

View full text Add to dashboard Cite

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“…It is difficult and very expensive to identify and correct software defects especially those corresponding to large projects. The majority of researches dealing with this issue focus on code defects identification by analyzing the software source code [1] [2]. These code defects are usually, the consequence of design defects that was propagated to the code.…”

Section: Introductionmentioning

confidence: 99%

Decision tree-based Design Defects Detection

et al. 2021

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Design defects affect project quality and hinder development and maintenance. Consequently, experts need to minimize these defects in software systems. A promising approach is to apply the concepts of refactoring at higher level of abstraction based on UML diagrams instead of code level. Unfortunately, we find in literature many defects that are described textually and there is no consensus on how to decide if a particular design violates model quality. Defects could be quantified as metrics based rules that represent a combination of software metrics. However, it is difficult to find manually the best threshold values for these metrics. In this paper, we propose a new approach to identify design defects at the model level using the ID3 decision tree algorithm. We aim to create a decision tree for each defect. We experimented our approach on four design defects: The Blob, Data class, Lazy class and Feature Envy defect, using 15 Object-Oriented metrics. The rules generated using decision tree give a very promising detection results for the four open source projects tested in this paper. In Lucene 1.4 project, we found that the precision is 67% for a recall of 100%. In general, the accuracy varies from 49%, reaching for Lucene 1.4 project 80%.INDEX TERMS Anti-patterns, Bad smells, Decision tree, Model refactoring, Object oriented metrics.

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