Class level fault prediction using software clustering

2013 ◽  
Author(s):  
Giuseppe Scanniello ◽  
Carmine Gravino ◽  
Andrian Marcus ◽  
Tim Menzies
Keyword(s):  
Fault Prediction ◽  
Software Clustering ◽  
Class Level

scholarly journals Software Metrics Evaluation Based on Entropy

2010 ◽  
pp. 139-151 ◽  
Author(s):  
R. Selvarani ◽  
T.R.Gopalakrishnan Nair ◽  
Muthu Ramachandran ◽  
Kamakshi Prasad
Keyword(s):  
Software Development ◽  
Software Metrics ◽  
Object Oriented ◽  
Fault Prediction ◽  
Design Metrics ◽  
Incremental Model ◽  
Class Level ◽  
Development Life Cycle ◽  
Agile Software ◽  
Fault Proneness

The complexity of modern software, the commercial constraints and the expectation for high quality product demands the accurate fault prediction based on OO design metrics in the class level in the early stages of software development. The object oriented class metrics are used as quality predictors in the entire OO software development life cycle even when a highly iterative, incremental model or agile software process is employed. Recent research has shown some of the OO design metrics are useful for predicting fault-proneness of classes. In this chapter the empirical validation of a set of metrics proposed by Chidamber and Kemerer is performed to assess their ability in predicting the software quality in terms of fault proneness and degradation. The authors have also proposed the design complexity of object-oriented software with Weighted Methods per Class metric (WMC-CK metric) expressed in terms of Shannon entropy, and error proneness.

scholarly journals Class level software fault prediction using step wise linear regression

2018 ◽  
Vol 7 (4) ◽  
pp. 2552
Author(s):  
Sumangali Patil ◽  
A. Nagaraja Rao ◽  
C. Shoba Bindu
Keyword(s):  
Linear Regression ◽  
Large Data ◽  
Fault Prediction ◽  
Data Sets ◽  
Data Set ◽  
Software Fault Prediction ◽  
Class Level ◽  
Forecast Models ◽  
Programming Quality ◽  
High Dimensional Datasets

Programming measurements was utilized for foreseeing issue in modules of programming ventures. Convenient forecast of flaws enhances programming quality and subsequently its dependability. In this paper, a framework towards subspace grouping of large data set was pro-posed at class level to minimize the error. We composed an iterative calculation for grouping of high dimensional datasets for improvement of a target work. At that point the bunched data sets were examined utilizing Step-Wise Linear Regression to investigate the relationship among a structure variable and the autonomous factors in order to anticipate of damaged and non-faulty classes. To evaluate the supportive-ness of the model, we drove a practical learning on the Attitude Survey Data. The proposed strategy specifically managed blunder variables and consequently gave precise fault prediction least standard error (0.003) when contrasted with the current technique (4.687). Root mean square error which measures the distinction between the assessed error and the real error was (0.8) in the proposed technique. The results demonstrated that the forecast models based on subspace clustering were essentially predominant to the current techniques.  

Study regarding the bio-motor level and health of children from gymnasium level

2020 ◽  
pp. 1-9
Author(s):  
Dan Alexandru Szabo
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Anthropometric Measurements ◽  
Average Height ◽  
Mathematical Methods ◽  
Health Development ◽  
Health Level ◽  
Class Level ◽  
Flat Feet ◽  
Weight Problems

The investigation started from the need to find the level of bio-motor and health development in our Gymnasium School “Unirea” from Târgu Mureş. The research was also focused on discovering the children with BMI problems and finding the link between obesity and apparition of flat feet, spin and knee deficiencies. The methods of research were mainly experimental, we used anthropometric measurements of height, weight, body mass index and analyzed the parameters using statically and mathematical methods. The location of the study was the gymnasium level of the National College “Unirea” from Târgu Mureş, and involved 16 selected children with an average age 12.69 years old, 4 children with weight problems selected from every class level. The results of the investigation showed that the average height of the sample was 162.7 cm, weight 71 kg and a BMI average of 26.6. The BMI analyzed showed that obesity is an important factor in the apparition of other deficiencies, among students that were measured we also found 5 cases of kyphosis, 5 of scoliosis and 6 cases of flat feet. Conclusions of the investigation showed that BMI in youth is an important parameter in establishing the health level of children from gymnasium level and in preventing the apparition of the spine and feet deficiencies.

The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

2016 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Fatemeh Alighardashi ◽  
Mohammad Ali Zare Chahooki
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Machine Learning Algorithms ◽  
Fault Prediction ◽  
Filter Method ◽  
Selection Methods ◽  
Software Projects ◽  
Software Fault Prediction ◽  
Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

scholarly journals Rolling Bearing Fault Prediction Method Based on QPSO-BP Neural Network and Dempster–Shafer Evidence Theory

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1094 ◽  
Author(s):  
Lanjun Wan ◽  
Hongyang Li ◽  
Yiwei Chen ◽  
Changyun Li
Keyword(s):  
Neural Network ◽  
Working Conditions ◽  
Bp Neural Network ◽  
Evidence Theory ◽  
Prediction Method ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Fault Classification ◽  
Bearing Fault ◽  
Hidden Layer

To effectively predict the rolling bearing fault under different working conditions, a rolling bearing fault prediction method based on quantum particle swarm optimization (QPSO) backpropagation (BP) neural network and Dempster–Shafer evidence theory is proposed. First, the original vibration signals of rolling bearing are decomposed by three-layer wavelet packet, and the eigenvectors of different states of rolling bearing are constructed as input data of BP neural network. Second, the optimal number of hidden-layer nodes of BP neural network is automatically found by the dichotomy method to improve the efficiency of selecting the number of hidden-layer nodes. Third, the initial weights and thresholds of BP neural network are optimized by QPSO algorithm, which can improve the convergence speed and classification accuracy of BP neural network. Finally, the fault classification results of multiple QPSO-BP neural networks are fused by Dempster–Shafer evidence theory, and the final rolling bearing fault prediction model is obtained. The experiments demonstrate that different types of rolling bearing fault can be effectively and efficiently predicted under various working conditions.

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