Rapid and Efficient Bug Assignment Using ELM for IOT Software

Yin, Ying; Xiang, Dong; Xu, Tiantian

doi:10.1109/access.2018.2869306

Cited by 17 publications

(14 citation statements)

References 31 publications

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“…Four more datasets are used for comparing our models with recent studies. One is the GNU compiler collection (GCC) 6 dataset, which is a small dataset that has 2102 bug reports with an average of 32 bug reports for each developer which was used in [11]. The 64% of bug reports are selected for training, and the rest is used for testing.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…They attributed higher accuracy to the controlled quality of the bug reports and characteristics such as stable and smaller developer pools for industrial projects. Yin et al [11] used tf-idf to extract features and apply a genetic algorithm-based optimized Extreme Learning Machine (ELM) for bug triage. The ELM is a type of feed-forward neural network and does not use the back-propagation algorithm [25].…”

Section: Related Studies and Motivationmentioning

confidence: 99%

“…• We compare three word-embedding techniques in the context of the bug triage problem: Word2Vec, GloVe, and ELMo. • We compare our implementation with others presented in recent deep learning-based studies, such as Deep Triage [3] , CNN-based approach [9], [10] and ELM based bug assignment method [11]. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

et al. 2020

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Bug triage processes are intended to assign bug reports to appropriate developers effectively, but they typically become bottlenecks in the development process-especially for large-scale software projects. Recently, several machine learning approaches, including deep learning-based approaches, have been proposed to recommend an appropriate developer automatically by learning past assignment patterns. In this paper, we propose a deep learning-based bug triage technique using a convolutional neural network (CNN) with three different word representation techniques: Word to Vector (Word2Vec), Global Vector (GloVe), and Embeddings from Language Models (ELMo). Experiments were performed on datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was measured as an evaluation metric. The experimental results suggest that the ELMo-based CNN approach performs best for the bug triage problem. GloVe-based CNN slightly outperforms Word2Vec-based CNN in many cases. Word2Vec-based CNN outperforms GloVe-based CNN when the number of samples per class in the dataset is high enough.

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Section: Evaluation and Resultsmentioning

confidence: 99%

Section: Related Studies and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

et al. 2020

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“…In [44] a hybrid approach of the Extreme Learning Method (ELM) and Genetic Algorithm (GA) is proposed. With extracting useful information from the fault reports, a vector space model is constructed based on the information and a minimal feature set is selected.…”

Section: Related Workmentioning

confidence: 99%

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

et al. 2020

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By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies. INDEX TERMS Internet of Things applications, fault prediction, formal verification, process analysis toolkit, multi-layer perceptron, particle swarm optimization.

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“…Many bug report triage methods [65][66][67][68][69][70] use data reduction. To achieve data reduction, these methods use KNN, naïve Bayes, and clustering and reduce feature selection and instance selection using the representative and statistic value of these methods or newly define "module selection.…”

Section: Bug Report Triagementioning

confidence: 99%

Improving bug report triage performance using artificial intelligence based document generation model

Lee

Seo

2020

Hum. Cent. Comput. Inf. Sci.

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Along with the fourth industrial revolution, artificial intelligence, big data, Internet of Things, and cloud computing are emerging as cutting-edge technologies globally. In particular, artificial intelligence has unlimited potential to further improve the quality of human life and can solve several difficult engineering problems [1-12]. Moreover, this technology provides basic ideas to derive successful solutions to numerous problems encountered in the software development field.

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Rapid and Efficient Bug Assignment Using ELM for IOT Software

Cited by 17 publications

References 31 publications

Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

Applying Convolutional Neural Networks With Different Word Representation Techniques to Recommend Bug Fixers

Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

Improving bug report triage performance using artificial intelligence based document generation model

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