scholarly journals Analysis of Developers’ Network and Change Burst Metrics as Predictors of Software Faults

2021 ◽  
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Fault Prediction ◽  
Software Projects ◽  
Software Faults ◽  
Detection Probabilities ◽  
Software Quality Metrics ◽  
Network Metrics ◽  
Metrics Model

Introduction: Many software quality metrics that can be used as proxies of measuring software quality by predicting software faults have previously been proposed. However determining a superior predictor of software faults given a set of metrics is difficult since prediction performances of the proposed metrics have been evaluated in non–uniform experimental contexts. There is need for software metrics that can guarantee consistent superior fault prediction performances across different contexts. Such software metrics would enable software developers and users to establish software quality. Objectives: This research sought to determine a predictor for software faults that requires least effort to detect software faults and has least cost of misclassifying software components as faulty or not given developers’ network metrics and change burst metrics. Methods: Experimental data for this study was derived from Jmeter, Gedit, POI and Gimp open source software projects. Logistic regression was used to predict faultiness of a file while linear regression was used to predict number of faults per file. Results: Change burst metrics model exhibited the highest fault detection probabilities with least cost of mis-classification of components as compared to the developers’ network model. Conclusion: The study found that change burst metrics could effectively predict software faults.

Important Issues in Software Fault Prediction

2014 ◽  
pp. 510-539 ◽  
Author(s):  
Golnoush Abaei ◽  
Ali Selamat
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Prediction Models ◽  
Research Field ◽  
Verification And Validation ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Software Fault Prediction ◽  
Learning Techniques ◽  
Software Fault

Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. In such cases, identifying and testing parts of the system that are more defect prone is reasonable. In fact, prediction models are mainly used for improving software quality and exploiting available resources. Software fault prediction is studied in this chapter based on different criteria that matters in this research field. Usually, there are certain issues that need to be taken care of such as different machine-learning techniques, artificial intelligence classifiers, variety of software metrics, distinctive performance evaluation metrics, and some statistical analysis. In this chapter, the authors present a roadmap for those researchers who are interested in working in this area. They illustrate problems along with objectives related to each mentioned criterion, which could assist researchers to build the finest software fault prediction model.

scholarly journals An Empirical Study of the Effect of Power Law Distribution on the Interpretation of OO Metrics

2013 ◽  
Vol 2013 ◽  
pp. 1-18 ◽  
Author(s):  
Raed Shatnawi ◽  
Qutaibah Althebyan
Keyword(s):  
Open Source ◽  
Power Law ◽  
Software Quality ◽  
Software Metrics ◽  
Prediction Models ◽  
Fault Prediction ◽  
Fat Tails ◽  
Threshold Values ◽  
Power Law Distribution ◽  
Open Source Systems

Context. Software metrics are surrogates of software quality. Software metrics can be used to find possible problems or chances for improvements in software quality. However, software metrics are numbers that are not easy to interpret. Previous analysis of software metrics has shown fat tails in the distribution. The skewness and fat tails of such data are properties of many statistical distributions and more importantly the phenomena of the power law. These statistical properties affect the interpretation of software quality metrics. Objectives. The objective of this research is to validate the effect of power laws on the interpretation of software metrics. Method. To investigate the effect of power law properties on software quality, we study five open-source systems to investigate the distribution and their effect on fault prediction models. Results. Study shows that power law behavior has an effect on the interpretation and usage of software metrics and in particular the CK metrics. Many metrics have shown a power law behavior. Threshold values are derived from the properties of the power law distribution when applied to open-source systems. Conclusion. The properties of a power law distribution can be effective in improving the fault-proneness models by setting reasonable threshold values.

Structural Code Clone Detection Methodology Using Software Metrics

2016 ◽  
Vol 26 (02) ◽  
pp. 307-332 ◽  
Author(s):  
Mehmet S. Aktas ◽  
Mustafa Kapdan
Keyword(s):  
Software Quality ◽  
Software Maintenance ◽  
Software Metrics ◽  
User Study ◽  
Quality Analysis ◽  
Clone Detection ◽  
Code Clones ◽  
Software Projects ◽  
Code Clone ◽  
Structural Code

Unnecessary repeated codes, also known as code clones, have not been well documented and are difficult to maintain. Code clones may become an important problem in the software development cycle, since any detected error must be fixed in all occurrences. This condition significantly increases software maintenance costs and requires effort/duration for understanding the code. This research introduces a novel methodology to minimize or prevent the code cloning problem in software projects. In particular, this manuscript is focused on the detection of structural code clones, which are defined as similarity in software structure such as design patterns. Our proposed methodology provides a solution to the class-level structural code clone detection problem. We introduce a novel software architecture that provides unification of different software quality analysis tools that take measurements for software metrics for structural code clone detection. We present an empirical evaluation of our approach and investigate its practical usefulness. We conduct a user study using human judges to detect structural code clones in three different open-source software projects. We apply our methodology to the same projects and compare results. The results show that our proposed solution is able to show high consistency compared with the results reached by the human judges. The outcome of this study also indicates that a uniform structural code clone detection system can be built on top of different software quality tools, where each tool takes measurements of different object-oriented software metrics.

Important Issues in Software Fault Prediction

2017 ◽  
pp. 162-190
Author(s):  
Golnoush Abaei ◽  
Ali Selamat
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Prediction Models ◽  
Research Field ◽  
Verification And Validation ◽  
Fault Prediction ◽  
Machine Learning Techniques ◽  
Software Fault Prediction ◽  
Learning Techniques ◽  
Software Fault

Quality assurance tasks such as testing, verification and validation, fault tolerance, and fault prediction play a major role in software engineering activities. Fault prediction approaches are used when a software company needs to deliver a finished product while it has limited time and budget for testing it. In such cases, identifying and testing parts of the system that are more defect prone is reasonable. In fact, prediction models are mainly used for improving software quality and exploiting available resources. Software fault prediction is studied in this chapter based on different criteria that matters in this research field. Usually, there are certain issues that need to be taken care of such as different machine-learning techniques, artificial intelligence classifiers, variety of software metrics, distinctive performance evaluation metrics, and some statistical analysis. In this chapter, the authors present a roadmap for those researchers who are interested in working in this area. They illustrate problems along with objectives related to each mentioned criterion, which could assist researchers to build the finest software fault prediction model.

Correlation Analysis of Software Defects Density and Metrics

2015 ◽  
Vol 713-715 ◽  
pp. 2225-2228
Author(s):  
Wei Zhang ◽  
Zhen Yu Ma ◽  
Wen Ge Zhang ◽  
Qing Ling Lu ◽  
Xiao Bing Nie
Keyword(s):  
Correlation Analysis ◽  
Software Quality ◽  
Software Metrics ◽  
Defect Density ◽  
Software Defects ◽  
Software Projects ◽  
Class Level ◽  
And Function

It is very useful for improving software quality if we can find which software metrics are more correlative with software defects or defects density. Based on 33 actual software projects, we analyzed 44 software metrics from application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defects, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density, these metrics can be used for improving software quality and predicting software defects density.

An Empirical Study on Software Fault Prediction Using Product and Process Metrics

2021 ◽  
Vol 14 (1) ◽  
pp. 62-78
Author(s):  
Raed Shatnawi ◽  
Alok Mishra
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Fault Prediction ◽  
Software Fault Prediction ◽  
Product Metrics ◽  
Software Models ◽  
Software Fault ◽  
Process Metrics ◽  
Product And Process ◽  
Fault Proneness

Product and process metrics are measured from the development and evolution of software. Metrics are indicators of software fault-proneness and advanced models using machine learning can be provided to the development team to select modules for further inspection. Most fault-proneness classifiers were built from product metrics. However, the inclusion of process metrics adds evolution as a factor to software quality. In this work, the authors propose a process metric measured from the evolution of software to predict fault-proneness in software models. The process metrics measures change-proneness of modules (classes and interfaces). Classifiers are trained and tested for five large open-source systems. Classifiers were built using product metrics alone and using a combination of product and the proposed process metric. The classifiers evaluation shows improvements whenever the process metrics were used. Evolution metrics are correlated with quality of software and helps in improving software quality prediction for future releases.

AN IMPLEMENTATION OF METRICS EXTRACTION AND ANALYZER TOOL

2007 ◽  
Vol 16 (01) ◽  
pp. 121-128 ◽  
Author(s):  
TONG-SENG QUAH
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Prediction Models ◽  
Data Access ◽  
Software Projects ◽  
Source Codes ◽  
Software Quality Management ◽  
Application Architecture ◽  
Present Information ◽  
Object Oriented Metrics

Nowadays, developers use N-tier application architecture which is a flexible and reusable structure for distribution to any number of client interfaces. In this study, we have implemented metrics extraction and analyzer tool for Business Tier and Data Access Tier source codes. We used existing object-oriented metrics and complexity software metrics as proposed for Business Tier code1,3. We use a set of metrics for the Data Access Tier that has been proposed in our previous study2. The primary aim of the implementation of the metric extraction and analyzer tool is to facilitate generation of software metrics which are used as independent variables in software quality prediction models. A secondary goal is to gather and present information on the attributes of source code to assist the programmers in managing the software projects. This tool is able to generate the principal components, correlation analysis report and descriptive statistics report. These analysis reports are essential reports for the study of software measurement and software quality management.

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 METRICS DEVELOPMENT FOR THE QUALIS OF SOFTWARE TECHNICAL PRODUCTION

2015 ◽  
Vol 42 (suppl 1) ◽  
pp. 73-75
Author(s):  
Marinho Jorge Scarpi
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Development Process ◽  
International Organization ◽  
System Behavior ◽  
Technical Standards ◽  
Quality Perception ◽  
Software Production ◽  
Technical Production ◽  
Intellectual Production

Objective: To recommend metrics to qualify software production and to propose guidelines for the CAPES quadrennial evaluation of the Post-Graduation Programs of Medicine III about this issue. Method: Identification of the development process quality features, of the product attributes and of the software use, determined by Brazilian Association of Technical Standards (ABNT), International Organization Standardization (ISO) and International Electrotechnical (IEC), important in the perspective of the CAPES Medicine III Area correlate users, basing the creation proposal of metrics aiming to be used on four-year evaluation of Medicine III. Results: The in use software quality perception by the user results from the provided effectiveness, productivity, security and satisfaction that originate from its characteristics of functionality, reliability, usability, efficiency, maintainability and portability (in use metrics quality). This perception depends on the specific use scenario. Conclusion: The software metrics should be included in the intellectual production of the program, considering the system behavior measurements results obtained by users' performance evaluation through out the favorable responses punctuation sum for the six in use metrics quality (27 sub-items, 0 to 2 points each) and for quality perception proof (four items, 0 to 10 points each). It will be considered as very good (VG) 85 to 94 points; good (G) 75 to 84 points; regular (R) 65 to 74 points; weak (W) 55 to 64 points; poor (P) <55 points.

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