Software Defect-Based Prediction Using Logistic Regression: Review and Challenges

Goyal, Jayanti; Sinha, Ripu Ranjan Sinha Ripu Ranjan

doi:10.1007/978-981-16-4641-6_20

Cited by 8 publications

(6 citation statements)

References 25 publications

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“…Logistic regression [45] (LR) is a powerful and efficient method that analyzes the effect of a set of independent variables on binary outcomes by quantifying the unique contribution of each independent variable. LR is the preferred method for binary classification tasks.…”

Section: Logistic Regressionmentioning

confidence: 99%

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Huang

et al. 2023

Big Data Min. Anal.

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The thermal comfort of passengers in the carriage cannot be ignored. Thus, this research aims to establish a prediction model for the thermal comfort of the internal environment of a subway car and find the optimal input combination in establishing the prediction model of the predicted mean vote (PMV) index. Data-driven modeling utilizes data from experiments and questionnaires conducted in Nanjing Metro. Support vector machine (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were used to build four models. This research aims to select the most appropriate input variables for the predictive model. All possible combinations of 11 input variables were used to determine the most accurate model, with variable selection for each model comprising 102 350 iterations. In the PMV prediction, the RF model was the best when using the correlation coefficients square (R 2 / as the evaluation indicator (R 2 : 0.7680, mean squared error (MSE): 0.2868). The variables include clothing temperature (CT), convective heat transfer coefficient between the surface of the human body and the environment (CHTC), black bulb temperature (BBT), and thermal resistance of clothes (TROC). The RF model with MSE as the evaluation index also had the highest accuracy (R 2 : 0.7676, MSE: 0.2836). The variables include clothing surface area coefficient (CSAC), CT, BBT, and air velocity (AV). The results show that the RF model can efficiently predict the PMV of the subway car environment.

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Section: Logistic Regressionmentioning

confidence: 99%

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Huang

et al. 2023

Big Data Min. Anal.

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“…The authors of [4] discussed and compared several research studies and systems that use Logistic Regression. They identified and categorized measuring techniques, including metrics, features, parameters, classifiers, accuracy, and data sets.…”

Section: Related Workmentioning

confidence: 99%

Software Defect Prediction Approaches Revisited

Shebl,

Afify,

Badr

2023

IJICIS

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A crucial field in software development and testing is Software Defect Prediction (SDP) because the quality, dependability, efficiency, and cost of the software are all improved by forecasting software defects at an earlier stage. Many existing models predict defects to facilitate software testing process for testers. A comprehensive review of these models from different perspectives is crucial to help new researchers enter this field and learn about its latest developments. Algorithms, method types, datasets, and tools were the only perspectives discussed in the current literature. A comprehensive study that takes into account a wide spectrum of viewpoints hasn't yet been published. Examining the development and advancement of SDP-related studies is the goal of this literature review. It provides a comprehensive and updated state-of-the-art that satisfies all stated criteria. Out of 591 papers retrieved from 6 reputable databases, 73 papers were eligible for analysis. This review addresses relevant research questions regarding techniques & method types, data details, tools, code syntax, semantics, structural and domain information. Motivation to conduct this comprehensive review is to equip the readers with the necessary information and keep them informed about the software defect prediction domain.

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“…Compared to the machine learning model, we choose the commonly used machine learning algorithms in defect detection, namely Logistic Regression (LR) [50], Transfer Naive Bayes (TNB) [51], Support Vector Machine (SVM) [52], Decision Tree (DT) [41]. The attribute information provided by the PROMISE dataset is used as the machine learning model input, which is also used in many traditional machine learning-based defect code detection work.…”

Section: Efficiency Of Defect Detection Based On Gcnnmentioning

confidence: 99%

BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

Wang

Tong

et al. 2022

Complex Intell. Syst.

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Since defects in software may cause product fault and financial loss, it is essential to conduct software defect prediction (SDP) to identify the potentially defective modules, especially in the early stage of the software development lifecycle. Recently, cross-version defect prediction (CVDP) began to draw increasing research interests, employing the labeled defect data of the prior version within the same project to predict defects in the current version. As software development is a dynamic process, the data distribution (such as defects) during version change may get changed. Recent studies utilize machine learning (ML) techniques to detect software defects. However, due to the close dependencies between the updated and unchanged code, prior ML-based methods fail to model the long and deep dependencies, causing a high false positive. Furthermore, traditional defect detection is performed on the entire project, and the detection efficiency is relatively low, especially on large-scale software projects. To this end, we propose BugPre, a CVDP approach to address these two issues. BugPre is a novel framework that only conducts efficient defect prediction on changed modules in the current version. BugPre utilizes variable propagation tree-based associated analysis method to obtain the changed modules in the current version. Besides, BugPre constructs graph leveraging code context dependences and uses a graph convolutional neural network to learn representative characteristics of code, thereby improving defect prediction capability when version changes occur. Through extensive experiments on open-source Apache projects, the experimental results indicate that our BugPre outperforms three state-of-the-art defect detection approaches, and the F1-score has increased by higher than 16%.

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Software Defect-Based Prediction Using Logistic Regression: Review and Challenges

Cited by 8 publications

References 25 publications

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Predicted Mean Vote of Subway Car Environment Based on Machine Learning

Software Defect Prediction Approaches Revisited

BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

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