A Novel Software Fault Prediction Approach To Predict Error-type Proneness in the Java Programs Using Stream X-Machine and Machine Learning

Ogunshile, Emmanuel; Phung, Khoa; Aydın, Mehmet Emin

doi:10.1109/conisoft52520.2021.00032

Cited by 3 publications

(13 citation statements)

References 35 publications

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“…In 2021 [18], we proposed a novel SFP approach using a streamlined process linking Stream X-Machine and machine learning techniques to predict if software modules are prone to having a particular type of runtime error in Java programs. Particularly, Stream X-Machine is used to model and generate test cases for different types of JREs, which will be employed to extract error-type data (ESM values) from the source codes.…”

Section: Related Workmentioning

confidence: 99%

“…In [18], we proposed a novel SFP approach for predicting error-type proneness in software modules using a streamlined process linking Stream X-Machine (a formal method) [19] and machine learning techniques. In particular, Stream X-Machine is used to model and generate test cases for different types of Java runtime errors, which will be employed to extract error-type data from the source codes.…”

Section: Introductionmentioning

confidence: 99%

“…Although the experimental results showed that the new datasets could significantly improve the performances of machine learning models in terms of predicting error-type proneness, there were some limitations and threats that could potentially hinder the findings of the study. Also, in [18], we only explored each ESM value individually for the purpose of predicting error-type proneness. In reality, this approach can be time consuming, especially during the training process because different prediction models will have to be trained for different types of runtime error.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…This paper is an extension of the study we proposed in [18]. In this research, we aim to overcome the existing limitations in the previous work [18] by: 1) Employing an industrial project that has accessible source code and is actively maintained to collect ESM values and software metrics for the experiments. More importantly, in this work, we primarily aim to present a novel set of software metrics, called Error-type (ET) software metrics, by considering the ESM values as one combined objective to capture the patterns of different types of Java runtime error (JRE).…”

Section: Introductionmentioning

confidence: 99%

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Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

2023

Self Cite

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In software development, identifying software faults is an important task. The presence of faults not only reduces the quality of the software, but also increases the cost of development life cycle. Fault identification can be performed by analysing the characteristics of the buggy source codes from the past and predict the present ones based on the same characteristics using statistical or machine learning models. Many studies have been conducted to predict the fault proneness of software systems. However, most of them provide either inadequate or insufficient information and thus make the fault prediction task difficult. In this paper, we present a novel set of software metrics called Error-type software metrics, which provides prediction models with information about patterns of different types of Java runtime error. Particular, in this study, the ESM values consist of information of three common Java runtime errors which are Index Out Of Bounds Exception, Null Pointer Exception, and Class Cast Exception. Also, we proposed a methodology for modelling, extracting, and evaluating error patterns from software modules using Stream X-Machine (a formal modelling method) and machine learning techniques. The experimental results showed that the proposed Error-type software metrics could significantly improve the performances of machine learning models in fault-proneness prediction.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

2023

Self Cite

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Predicting Software Faults Using Machine Learning Techniques: An Empirical Study

Gupta,

Sinha

2024

Data-Intensive Research

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A Novel Developed Supervised Machine Learning System For Classification And Prediction of Software Faults Using NASA Dataset

Gupta,

Sinha

2023

IJRITCC

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The software systems of modern computers are extremely complex and versatile. Therefore, it is essential to regularly detect and correct software design faults. In order to devote resources effectively towards the creation of trustworthy software, software companies are increasingly engaging in the practise of predicting fault-prone modules in advance of testing. These software fault prediction methods rely on the thoroughness with which prior software versions' fault as well as related code has been retrievedTime, energy, and money are all saved as a result. Increases the company's initial success and bottom line greatly by satisfying its clientele. Numerous academics have poured into this area throughout the years in an effort to raise the bar for all software. Nowadays, The most often used approaches in this field are those based on machine learning (ML). The field of ML seeks to perfect software capable of evolving as well as adapting in response to fresh data. This paper introduces a fresh approach for doing ML by bringing together a number of different expert systems. In order to reach agreement on which aspects of a software system need to be tested, the proposed multi-classifier model pools the strengths of the most effective classifiers. Several top-performing classifiers for defect prediction are put through their paces in an experiential evaluation. We test our method on 16 publicly available datasets from the NASA Metric Data Programme (MDP) repository at the promise repository. Parameters of confusion, recall, precision, recognition accuracy, etc., are evaluated and contrasted with existing schemes in a software analysis performed with the help of the python simulation tool with findings. The experimental outcomes demonstrate that by combining LGBM, XGBoost, and Voting classifiers, using a multi classifier approach, we are capable to significantly improve software fault prediction performance. The results of the investigation show that the suggested method will lead to better practical outcomes in the prediction of device failures.

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A Novel Software Fault Prediction Approach To Predict Error-type Proneness in the Java Programs Using Stream X-Machine and Machine Learning

Cited by 3 publications

References 35 publications

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

Error-Type—A Novel Set of Software Metrics for Software Fault Prediction

Predicting Software Faults Using Machine Learning Techniques: An Empirical Study

A Novel Developed Supervised Machine Learning System For Classification And Prediction of Software Faults Using NASA Dataset

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