Software Engineering Framework for Software Defect Management Using Machine Learning Techniques with Azure

2020 ◽  
pp. 155-183
Author(s):  
Uma Subbiah ◽  
Muthu Ramachandran ◽  
Zaigham Mahmood
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Machine Learning Techniques ◽  
Defect Management ◽  
Software Defect ◽  
Learning Techniques

scholarly journals Software Defect Estimation using Machine Learning Algorithms

2021 ◽  
Vol 10 (1) ◽  
pp. 204-208
Author(s):  
Mandi Akif Hussain* ◽  
Revoori Veeharika Reddy ◽  
Kedharnath Nagella ◽  
Vidya S.
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Computer Science ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
System Software ◽  
Software Defect ◽  
Learning Techniques ◽  
And Training

Software Engineering is a branch of computer science that enables tight communication between system software and training it as per the requirement of the user. We have selected seven distinct algorithms from machine learning techniques and are going to test them using the data sets acquired for NASA public promise repositories. The results of our project enable the users of this software to bag up the defects are selecting the most efficient of given algorithms in doing their further respective tasks, resulting in effective results.

A Literature Review Study of Software Defect Prediction using Machine Learning Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 300 ◽  
Author(s):  
Feidu Akmel ◽  
Ermiyas Birihanu ◽  
Bahir Siraj
Keyword(s):  
Machine Learning ◽  
Software Metrics ◽  
Quality Standard ◽  
Machine Learning Techniques ◽  
Software Systems ◽  
Health Care Insurance ◽  
Software Defect ◽  
Learning Techniques ◽  
Software Product

Software systems are any software product or applications that support business domains such as Manufacturing,Aviation, Health care, insurance and so on.Software quality is a means of measuring how software is designed and how well the software conforms to that design. Some of the variables that we are looking for software quality are Correctness, Product quality, Scalability, Completeness and Absence of bugs, However the quality standard that was used from one organization is different from other for this reason it is better to apply the software metrics to measure the quality of software. Attributes that we gathered from source code through software metrics can be an input for software defect predictor. Software defect are an error that are introduced by software developer and stakeholders. Finally, in this study we discovered the application of machine learning on software defect that we gathered from the previous research works.

SDP-ML: An Automated Approach of Software Defect Prediction employing Machine Learning Techniques

2021 ◽  
Author(s):  
Md Nasir Uddin ◽  
Bixin Li ◽  
Md Naim Mondol ◽  
Md Mostafizur Rahman ◽  
Md Suman Mia ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Software Defect ◽  
Learning Techniques

scholarly journals Binary Class Classification of Software Faults in Software Modules using Popular Machine Learning Techniques

2020 ◽  
Vol 6 (6) ◽  
pp. 14-18
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Regression Tree ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Customer Requirements ◽  
Software Faults ◽  
Learning Techniques ◽  
Software Modules

Software engineering is an important area that deals with development and maintenance of software. After developing a software, it is always important to track its performance. One has to always see whether the software functions according to customer requirements. To ensure this, faulty and non- faulty modules must be identified. For this purpose, one can make use of a model for binary class classification of faults. Different technique's outputs differ in one or the other way with respect to the following: fault dataset used, complexity, classification algorithm implemented, etc. Various machine learning techniques can be used for this purpose. But this paper deals with the best classification algorithms available till date and they are decision tree, random forest, naive bayes and logistic regression (tree-based techniques and bayesian based techniques). The motive behind developing such a project is to identify the faulty modules within a software before the actual software testing takes place. As a result, the time consumed by testers or the workload of the testers can be reduced to an extent. This work is very well useful to those working in software industry and also to those people carrying out research in software engineering where the lifecycle of development of a software is discussed.

Machine Learning Techniques to Predict Software Defect

2014 ◽  
pp. 1422-1434 ◽  
Author(s):  
Ramakanta Mohanty ◽  
Vadlamani Ravi
Keyword(s):  
Machine Learning ◽  
Feature Subset Selection ◽  
Machine Learning Techniques ◽  
Group Method ◽  
Software Project ◽  
Feature Subset ◽  
Software Defects ◽  
Software Defect ◽  
Learning Techniques ◽  
Sensitivity Specificity

The past 10 years have seen the prediction of software defects proposed by many researchers using various metrics based on measurable aspects of source code entities (e.g. methods, classes, files or modules) and the social structure of software project in an effort to predict the software defects. However, these metrics could not predict very high accuracies in terms of sensitivity, specificity and accuracy. In this chapter, we propose the use of machine learning techniques to predict software defects. The effectiveness of all these techniques is demonstrated on ten datasets taken from literature. Based on an experiment, it is observed that PNN outperformed all other techniques in terms of accuracy and sensitivity in all the software defects datasets followed by CART and Group Method of data handling. We also performed feature selection by t-statistics based approach for selecting feature subsets across different folds for a given technique and followed by the feature subset selection. By taking the most important variables, we invoked the classifiers again and observed that PNN outperformed other classifiers in terms of sensitivity and accuracy. Moreover, the set of ‘if- then rules yielded by J48 and CART can be used as an expert system for prediction of software defects.

scholarly journals The effects of applying filters on EEG signals for classifying developers’ code comprehension

2021 ◽  
Vol 19 (6) ◽  
pp. 584-602
Author(s):  
Lucian Jose Gonçales ◽  
Kleinner Farias ◽  
Lucas Kupssinskü ◽  
Matheus Segalotto
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Random Forest ◽  
Random Forest Classifier ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
F Measure

EEG signals are a relevant indicator for measuring aspects related to human factors in Software Engineering. EEG is used in software engineering to train machine learning techniques for a wide range of applications, including classifying task difficulty, and developers’ level of experience. The EEG signal contains noise such as abnormal readings, electrical interference, and eye movements, which are usually not of interest to the analysis, and therefore contribute to the lack of precision of the machine learning techniques. However, research in software engineering has not evidenced the effectiveness when applying these filters on EEG signals. The objective of this work is to analyze the effectiveness of filters on EEG signals in the software engineering context. As literature did not focus on the classification of developers’ code comprehension, this study focuses on the analysis of the effectiveness of applying EEG filters for training a machine learning technique to classify developers' code comprehension. A Random Forest (RF) machine learning technique was trained with filtered EEG signals to classify the developers' code comprehension. This study also trained another random forest classifier with unfiltered EEG data. Both models were trained using 10-fold cross-validation. This work measures the classifiers' effectiveness using the f-measure metric. This work used the t-test, Wilcoxon, and U Mann Whitney to analyze the difference in the effectiveness measures (f-measure) between the classifier trained with filtered EEG and the classifier trained with unfiltered EEG. The tests pointed out that there is a significant difference after applying EEG filters to classify developers' code comprehension with the random forest classifier. The conclusion is that the use of EEG filters significantly improves the effectivity to classify code comprehension using the random forest technique.

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