Software defect prediction based on correlation weighted class association rule mining

Shao, Yuanxun; Liu, Bin; Wang, Shihai; Li, Guoqi

doi:10.1016/j.knosys.2020.105742

Cited by 48 publications

(22 citation statements)

References 46 publications

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“…They determined weights of features using correlation analysis. They proved GMean measure has been increased with their approach [8]. Shuo Feng et al proposed complexity based over sampling technique to address data imbalance problem in identification of software defects [9].…”

Section: Literature Surveymentioning

confidence: 99%

An Hybrid Approach for Cost Effective Prediction of Software Defects

Maddipati¹,

Srinivas²

2021

IJACSA

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Identifying software defects during early stages of Software Development life cycle reduces the project effort and cost. Hence there is a lot of research done in finding defective proneness of a software module using machine learning approaches. The main problems with software defect data are cost effective and imbalance. Cost effective problem refers to predicting defective module as non defective induces high penalty compared to predicting non defective module as defective. In our work, we are proposing a hybrid approach to address cost effective problem in Software defect data. To address cost effective problem, we used bagging technique with Artificial Neuro Fuzzy Inference system as base classifier. In addition to that, we also addressed Class Imbalance & High dimensionality problems using Artificial Neuro Fuzzy inference system & principle component analysis respectively. We conducted experiments on software defect datasets, downloaded from NASA dataset repository using our proposed approach and compared with approaches mentioned in literature survey. We observed Area under ROC curve (AuC) for proposed approach was improved approximately 15% compared with highly efficient approach mentioned in literature survey.

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Section: Literature Surveymentioning

confidence: 99%

An Hybrid Approach for Cost Effective Prediction of Software Defects

Maddipati¹,

Srinivas²

2021

IJACSA

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“…Sun et al (2020) established a 0-1 matrix as well as weighted them according to the importance of projects and transactions and then extracted the hidden value data. Considering the importance of features in data, Shao et al (2020) proposed a prediction model of a mining algorithm based on correlation weighting.…”

Section: Literature Reviewmentioning

confidence: 99%

The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

Guo¹,

Hu²,

Ze-peng³

et al. 2021

Agric. Econ. - Czech

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With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.

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“…H. GAO, et. al., in 2019 emphasized on providing the novel idea to predict software defect with the help of complex network features for signifying defect information [10]. First of all, the selection of eighteen versions of nine open source projects had done using certain rules.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Mutation Testing Using Hybrid Metaheuristic Technique With Machine Learning for Software Defect Prediction

2021

IJESRT

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Software defect prediction model based on the mutation testing is a pioneering method for the fault-based unit testing in which faults are detected by executing certain test data. This paper presents an Optimized Mutation Testing (OMT) technique based software defect prediction model using the concept of hybrid metaheuristic technique. Here, hybridization of OMT with Enhanced Learning-to-Rank (ELTR) is used for the feature extraction from mutation testing based data generation mechanism. In the proposed approach, first hybrid technique is used for the test data feature extraction then this data is exercised to cover all mutants present in the specific program under test and then machine learning based Random Forest as an ensemble classifier is used as a classifier. The proposed method can improve the testing as well defect prediction efficiency by deleting the redundant test data. In this research work, two models are implemented for the software defect prediction using the ELTR and LTR. At last, the performance parameters such as Detection Rate, Defect Predication Value, Execution Time, Percentage of Fault Negative Rate and Percentage of Fault Rate are measured and compared with the existing work to validate the proposed model.

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Software defect prediction based on correlation weighted class association rule mining

Cited by 48 publications

References 46 publications

An Hybrid Approach for Cost Effective Prediction of Software Defects

An Hybrid Approach for Cost Effective Prediction of Software Defects

The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

An Optimized Mutation Testing Using Hybrid Metaheuristic Technique With Machine Learning for Software Defect Prediction

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