scholarly journals An Empirical Assessment and Validation of Redundancy Metrics Using Defect Density as Reliability Indicator

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Dalila Amara ◽  
Ezzeddine Fatnassi ◽  
Latifa Ben Arfa Rabai
Keyword(s):  
Software Reliability ◽  
Software Metrics ◽  
Defect Density ◽  
Quantitative Model ◽  
Internal Quality ◽  
Entropy Measure ◽  
Quality Factors ◽  
Reliability Indicators ◽  
Software Defect ◽  
Reliability Indicator

Software metrics which are language-dependent are proposed as quantitative measures to assess internal quality factors for both method and class levels like cohesion and complexity. The external quality factors like reliability and maintainability are in general predicted using different metrics of internal attributes. Literature review shows a lack of software metrics which are proposed for reliability measurement and prediction. In this context, a suite of four semantic language-independent metrics was proposed by Mili et al. (2014) to assess program redundancy using Shannon entropy measure. The main objective of these metrics is to monitor program reliability. Despite their important purpose, they are manually computed and only theoretically validated. Therefore, this paper aims to assess the redundancy metrics and empirically validate them as significant reliability indicators. As software reliability is an external attribute that cannot be directly evaluated, we employ other measurable quality factors that represent direct reflections of this attribute. Among these factors, defect density is widely used to measure and predict software reliability based on software metrics. Therefore, a linear regression technique is used to show the usefulness of these metrics as significant indicators of software defect density. A quantitative model is then proposed to predict software defect density based on redundancy metrics in order to monitor software reliability.

scholarly journals Fuzzy Logic Based Model to Predict Per Phase Software Defect

2019 ◽  
Vol 8 (9S) ◽  
pp. 36-41 ◽  
Keyword(s):  
Fuzzy Logic ◽  
Software Reliability ◽  
Software Metrics ◽  
Defect Density ◽  
Performance Measure ◽  
Efficient Manner ◽  
Density Prediction ◽  
Software Defect ◽  
Time Period ◽  
Proposed Model

Software reliability is expressed as the probability of software to function properly under specified condition for a specified time period. A basic method to evaluate the software reliability is to check the presence of defects in the software. The presence of defect can be calculated as defect density measured defined as total number of defects present in the software divided by the size of the software. The paper proposes a fuzzy logic based model to predict per phase software defect density. The model uses 3 relevant software metrics per SDLC phase. Defect density prediction is a useful measure, which indicates the critical modules of the project and helps software teams to plan their resources in an efficient manner. The proposed model results are better in comparison with existing literature in the same domain when compared using MRE performance measure on 20 project dataset.

Research on Software Defect Prediction Method Based on Machine Learning

2014 ◽  
Vol 687-691 ◽  
pp. 2182-2185 ◽  
Author(s):  
Wei Zhang ◽  
Zhen Yu Ma ◽  
Qing Ling Lu ◽  
Xiao Bing Nie ◽  
Juan Liu
Keyword(s):  
Machine Learning ◽  
Correlation Analysis ◽  
Software Metrics ◽  
Defect Density ◽  
Rank Correlation ◽  
Prediction Method ◽  
Learning Models ◽  
Software Projects ◽  
Software Defect ◽  
Machine Learning Models

This paper analyzed 44 metrics of 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 defect, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density. On the basis of feature selection, we predicted defect density with 16 machine learning models for 33 actual software projects. The results show that the Spearman rank correlation coefficient (SRCC) between the predicting defect density and the actual defect density based on SVR model is 0.6727, higher than other 15 machine learning models, the model that has the second absolute value of SRCC is IBk model, the SRCC only is-0.3557, the results show that the method based on SVR has the highest prediction accuracy.

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.

Connecting Software Reliability Growth Models to Software Defect Tracking

2020 ◽  
Author(s):  
Maskura Nafreen ◽  
Melanie Luperon ◽  
Lance Fiondella ◽  
Vidhyashree Nagaraju ◽  
Ying Shi ◽  
...  
Keyword(s):  
Software Reliability ◽  
Growth Models ◽  
Reliability Growth ◽  
Software Defect ◽  
Software Reliability Growth Models ◽  
Software Reliability Growth

scholarly journals A New Framework Consisted of Data Preprocessing and Classifier Modelling for Software Defect Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Haijin Ji ◽  
Song Huang
Keyword(s):  
Software Metrics ◽  
Data Preprocessing ◽  
Defect Prediction ◽  
Data Repository ◽  
Software Defect Prediction ◽  
Software Projects ◽  
Software Defect ◽  
Nonnormal Distribution ◽  
Non Gaussian ◽  
New Framework

Different data preprocessing methods and classifiers have been established and evaluated earlier for the software defect prediction (SDP) across projects. These novel approaches have provided relatively acceptable prediction results for different software projects. However, to the best of our knowledge, few researchers have combined data preprocessing and building robust classifier simultaneously to improve prediction performances in SDP. Therefore, this paper presents a new whole framework for predicting fault-prone software modules. The proposed framework consists of instance filtering, feature selection, instance reduction, and establishing a new classifier. Additionally, we find that the 21 main software metrics commonly do follow nonnormal distribution after performing a Kolmogorov-Smirnov test. Therefore, the newly proposed classifier is built on the maximum correntropy criterion (MCC). The MCC is well-known for its effectiveness in handling non-Gaussian noise. To evaluate the new framework, the experimental study is designed with due care using nine open-source software projects with their 32 releases, obtained from the PROMISE data repository. The prediction accuracy is evaluated using F-measure. The state-of-the-art methods for Cross-Project Defect Prediction are also included for comparison. All of the evidences derived from the experimentation verify the effectiveness and robustness of our new framework.

Performance Measurement for New Mobile Data Services During Generation Upgrading Period: A Case of China's Telecom Industry

2021 ◽  
Author(s):  
Yu Du ◽  
Xiaohang Zhang ◽  
Zhengren Li ◽  
Yijun Guo
Keyword(s):  
Performance Measurement ◽  
Quantitative Model ◽  
Service Development ◽  
Data Service ◽  
Mobile Data ◽  
Mobile Data Services ◽  
Data Services ◽  
Reliability Indicators ◽  
Mobile Data Service ◽  
New Generation

Abstract For the global telecom operators, mobile data services have gradually taken the part of traditional voice services to become the main revenue growth point. However, during the upgrading period of new generation networks (Such as 5G), new mobile data services are still at the stage of exploration, the network capabilities and the application scenarios are unmatured. In this phase, it is incomplete and misleading to simply measure the performance of new services from one dimension, such as data traffic or revenue, and the measurement should be dynamically changed according to the development of the new services. Therefore, telecom operators want to improve the existing performance measurement from the aspect of integrity and dynamics. In this paper, we propose Mobile-data-service Development Index (MDDI), and build a quantitative model to dynamic measure the overall performance of mobile data services. To approach a fuller understanding, we creatively bring investment indicators and networks reliability indicators into performance indicators system, and discuss the relationships among subindices and the selection of outcome criteria in MDDI. In the part of empirical research, we use the model to analyze the dynamic characteristics of a new mobile data service in China, and summarize the development strategies of every stage. The findings can also give guidelines for new services of 5G and other new generation networks in the future.

Software Metrics and Design Quality in Object Oriented Paradigm

2011 ◽  
pp. 256-272
Author(s):  
Gopalakrishnan T.R. Nair ◽  
Selvarani R
Keyword(s):  
Risk Assessment ◽  
Software Metrics ◽  
Research Effort ◽  
Object Oriented ◽  
Object Oriented Programming ◽  
Quality Factors ◽  
Engineering Software ◽  
Building Models ◽  
Future Challenges ◽  
Significant Research

As the object oriented programming languages and development methodologies moved forward, a significant research effort was spent in defining specific approaches and building models for quality based on object oriented measurements. Software metrics research and practice have helped in building an empirical basis for software engineering. Software developers require objectives and valid measurement schemes for the evaluation and improvisation of product quality from the initial stages of development. Measuring the structural design properties of a software system such as coupling, inheritance, cohesion, and complexity is a promising approach which can lead to an early quality assessment. The class codes and class diagrams are the key artifacts in the development of object oriented (OO) software and it constitutes the backbone of OO development. It also provides a solid foundation for the design and development of software with a greater influence over the system that is implemented. This chapter presents a survey of existing relevant works on class code / class diagram metrics in an elaborate way. Here, a critical review of the existing work is carried out in order to identify the lessons learnt regarding the way these studies are performed and reported. This work facilitates the development of an empirical body of knowledge. The classical approaches based on statistics alone do not provide managers and developers with a decision support scheme for risk assessment and cost reduction. One of the future challenges is to use software metrics in a way that they creatively address and handle the key objectives of risk assessment and the estimation of external quality factors of the software.

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