Proceedings of the 4th ACM SIGSOFT International Workshop on Machine-Learning Techniques for Software-Quality Evaluation

2020 ◽  
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Quality Evaluation ◽  
International Workshop ◽  
Machine Learning Techniques ◽  
Software Quality Evaluation ◽  
Learning Techniques

Overview of Machine Learning Approaches

2017 ◽  
pp. 19-33
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Case Based Reasoning ◽  
Learning Approaches ◽  
Software Module ◽  
Learning Techniques ◽  
Case Based

This chapter enlists and presents an overview of various machine learning approaches. It also explains the machine learning techniques used in the area of software engineering domain especially case-based reasoning method. Case-based reasoning is used to predict software quality of the system by examining a software module and predicting whether it is faulty or non-faulty. In this chapter an attempt has been made to propose a model with the help of previous data which is used for prediction. In this chapter, how machine learning technique such as case-based reasoning has been used for error estimation or fault prediction. Apart from case-based reasoning, some other types of learning methods have been discussed in detail.

scholarly journals Prediction of Software Design Defect using Enhanced Machine Learning Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 2462-2465
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Software Design ◽  
Software Package ◽  
Machine Learning Techniques ◽  
Software Project ◽  
Software Projects ◽  
Design Defect ◽  
Learning Techniques ◽  
The Cost

Prediction of software detection is most widely used in many software projects and this will improve the software quality, reducing the cost of the software project. It is very important for the developers to check every package and code files within the project. There are two classifiers that are present in the Software Package Defect (SPD) prediction that can be divided as Defect–prone and not-defect-prone modules. In this paper, the merging of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive craniologist Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The comparitive analysis can be shown in between the three algorithms and also individually.

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