scholarly journals Impact of restricted forward greedy feature selection technique on bug prediction

2015 ◽  
Author(s):  
Muthukumaran Kasinathan ◽  
Lalita Bhanu Murthy Neti
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Source Code ◽  
Feature Selection Technique ◽  
Code Metrics ◽  
Change Metrics ◽  
Misclassification Rates ◽  
The Individual ◽  
The Impact ◽  
Source Code Metrics

Several change metrics and source code metrics have been introduced and proved to be effective in bug prediction. Researchers performed comparative studies of bug prediction models built using the individual metrics as well as combination of these metrics. In this paper, we investigate the impact of feature selection in bug prediction models by analyzing the misclassification rates of these models with and without feature selection in place. We conduct our experiments on five open source projects by considering numerous change metrics and source code metrics. And this study aims to figure out the reliable subset of metrics that are common amongst all projects.

scholarly journals Impact of restricted forward greedy feature selection technique on bug prediction

2015 ◽  
Author(s):  
Muthukumaran Kasinathan ◽  
Lalita Bhanu Murthy Neti
Keyword(s):  
Feature Selection ◽  
Prediction Models ◽  
Source Code ◽  
Feature Selection Technique ◽  
Code Metrics ◽  
Change Metrics ◽  
Misclassification Rates ◽  
The Individual ◽  
The Impact ◽  
Source Code Metrics

Several change metrics and source code metrics have been introduced and proved to be effective in bug prediction. Researchers performed comparative studies of bug prediction models built using the individual metrics as well as combination of these metrics. In this paper, we investigate the impact of feature selection in bug prediction models by analyzing the misclassification rates of these models with and without feature selection in place. We conduct our experiments on five open source projects by considering numerous change metrics and source code metrics. And this study aims to figure out the reliable subset of metrics that are common amongst all projects.

scholarly journals A public unified bug dataset for java and its assessment regarding metrics and bug prediction

2020 ◽  
Vol 28 (4) ◽  
pp. 1447-1506 ◽  
Author(s):  
Rudolf Ferenc ◽  
Zoltán Tóth ◽  
Gergely Ladányi ◽  
István Siket ◽  
Tibor Gyimóthy
Keyword(s):  
Prediction Models ◽  
Source Code ◽  
Decision Tree Algorithm ◽  
Large Dataset ◽  
Code Analysis ◽  
Project Learning ◽  
Code Metrics ◽  
Public Datasets ◽  
Source Code Metrics ◽  
Cross Project

AbstractBug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metric-based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction. We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction. We found that there are statistically significant differences in the values of the original and the newly calculated metrics; furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.

Source Code Metrics to Predict the Properties of FPGA/VHDL-Based Synthesized Products

2018 ◽  
Author(s):  
Oscar E. Perez-Cham ◽  
Carlos Soubervielle- Montalvo ◽  
Alberto S. Nunez-Varela ◽  
Cesar Puente ◽  
Luis J. Ontanon-Garcia
Keyword(s):  
Source Code ◽  
Code Metrics ◽  
Source Code Metrics

scholarly journals A Study of the Relationships between Source Code Metrics and Attractiveness in Free Software Projects

2010 ◽  
Author(s):  
Paulo Meirelles ◽  
Carlos Santos Jr. ◽  
Joao Miranda ◽  
Fabio Kon ◽  
Antonio Terceiro ◽  
...  
Keyword(s):  
Source Code ◽  
Free Software ◽  
Software Projects ◽  
Code Metrics ◽  
Source Code Metrics

scholarly journals An Empirical Analysis on Effectiveness of Source Code Metrics for Aging Related Bug Prediction

2019 ◽  
Vol 2019 (2) ◽  
pp. 117-126
Author(s):  
Chinmay Hota ◽  
Lov Kumar ◽  
Lalita Bhanu Murthy Neti
Keyword(s):  
Empirical Analysis ◽  
Source Code ◽  
Code Metrics ◽  
Source Code Metrics

scholarly journals Data stream mining for predicting software build outcomes using source code metrics

2014 ◽  
Vol 56 (2) ◽  
pp. 183-198 ◽  
Author(s):  
Jacqui Finlay ◽  
Russel Pears ◽  
Andy M. Connor
Keyword(s):  
Data Stream ◽  
Source Code ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Code Metrics ◽  
Source Code Metrics
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