[PDF] Top 20 Predicting Software Fault Proneness Using Machine Learning
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Predicting Software Fault Proneness Using Machine Learning
... any software application is delivered to the clients, it has to be tested so that it is verified that it meets its functional and non-functional ...requirements. Software testing is often paired with ... See full document
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Adaptive Approach of Fault Prediction in Software Modules by using Discriminative and Generative Model of Machine Learning
... The Weka workbench is a collection of machine learning algorithms and data preprocessing tools. Weka was developed at the University of Waikato in New Zealand, and the name stands for Waikato Environment ... See full document
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Predicting the Heart Disease's using Machine Learning Techniques
... in software engineering has gotten huge open doors diverse regions, restorative discipline is one of the pitches where the gadget of software engineering can be ...of software engineering changes ... See full document
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Predicting Malnutrition Disease Using Various Machine Learning Algorithms
... In this segment to loading the dataset of Malnutrition of different patients of cities of Madhya Pradesh. All the dataset is loaded in WEKA 3.8 toolkit software to analysis the regression model. We store the data ... See full document
6
Predicting learner knowledge of individual words using machine learning
... well-known machine-learning algorithms using scikit-learn (Pedregosa et ...Vector Machine (SVM) with var- ious kernels, k Nearest Neighbors, Logistic Re- gression, and Random ... See full document
9
Machine learning based methodology for testing object oriented applications
... of software testing is to find the maximum number of errors as early as possible in the software development ...phase. Software testing ensures the quality of the source code and testing ... See full document
6
Predicting software maintainability in object-oriented systems using ensemble techniques
... for software success, however it is a challenging task to ...several machine learning models have been applied with variable results and no clear indication of which techniques are more ...major ... See full document
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Software defect prediction using enhanced relevance vector machine
... in software defect prediction [7, ...and machine learning algorithms have been ...for software defect ...in software defect ...for predicting module that was likely to have ... See full document
5
Predicting Employee Sustainability Using Machine Learning
... Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly ...of machine ... See full document
5
Risk chain prediction metrics for predicting fault proneness in Software Systems
... in software. Software developers utilize the comments and identifiers to represent elements of the problem or solution domain [11, ...among software artifacts [1, 13, 30], software clustering ... See full document
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Software Fault Proneness Prediction Using Support Vector Machines
... Planning and resource allocating for inspection and testing is difficult and it is usually done on empirical basis. The model predicted in the above section could be of great help for planning and executing testing ... See full document
6
Stock Market Forecasting Using Machine Learning
... come. using keyword we separate data from huge data more specifically proposed algorithm has defined the whole relationships embedded in social media as a graph with several layers proposed algorithms have a ... See full document
11
Predicting Outcome of Judicial Cases and Analysis using Machine Learning
... It is a supervised learning algorithm where value of k is predefined and changes in the value of k will give rise to variations in the performance metrics. In this algorithm, data points are plotted according to ... See full document
5
Smart Stick for Blind using Machine Learning
... The given model has supersonic device and water sensor hooked up thereto. If any of the device is triggered then the there'll be vibration. The blind man just in case if he's any emergency will endure the emergency ... See full document
7
Predicting Outcomes of Chess Endgames Using Machine Learning Algorithms
... Vector Machine: Support vector Machine constructs a hyperplane or set of hyperplanes in a high or infinite dimensional space which can be used for classification, regression or other tasks like outlier ... See full document
6
A Fuzzified Approach for the Prediction of Fault Proneness and Defect Density
... on software metrics ...build fault free and stupendous softwares; and to make them fault free object oriented metrics are being ...the software productivity. CK metrics can be used to obtain ... See full document
6
Software Defect Prediction Using Enhanced Machine Learning Technique
... where software modules are classified as either defect- prone or not-defect-prone modules, using a set of software ...common software metrics include: cyclomatic complexity, halstead ... See full document
5
Using Machine Learning Techniques to Predict Introductory Programming Performance
... six machine learning algorithms for predicting programming success, using the pre-determined factors, is ...vector machine), back propagation (artificial neural network) and ... See full document
6
Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines
... Keywords: multiple fault detection; rotating electrical machines; drive systems; multi-label 20.. classification; machine learning; fault severity; fault classifiers 21.[r] ... See full document
17
The Role of Machine Learning in Internet of Things (IoT) Research: A Review
... of machine learning with ...with machine learning such that agriculture, security, industry, health monitoring, traffic analysis, social ...category, machine learning operations ... See full document
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