scholarly journals Analysis of Software Quality Using Software Metrics

2018 ◽  
Vol 8 (4/5) ◽  
pp. 11-20 ◽  
Author(s):  
Ermiyas Birihanu Belachew
Keyword(s):  
Software Quality ◽  
Software Metrics

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.

AN EMPIRICAL STUDY OF SOFTWARE METRICS FOR ASSESSING THE PHASES OF AN AGILE PROJECT

2012 ◽  
Vol 22 (04) ◽  
pp. 525-548 ◽  
Author(s):  
GIULIO CONCAS ◽  
MICHELE MARCHESI ◽  
GIUSEPPE DESTEFANIS ◽  
ROBERTO TONELLI
Keyword(s):  
Software Quality ◽  
Web Application ◽  
Software Metrics ◽  
Pair Programming ◽  
Application Development ◽  
Agile Practices ◽  
Code Metrics ◽  
Agile Process ◽  
Adoption Level

We present an analysis of the evolution of a Web application project developed with object-oriented technology and an agile process. During the development we systematically performed measurements on the source code, using software metrics that have been proved to be correlated with software quality, such as the Chidamber and Kemerer suite and Lines of Code metrics. We also computed metrics derived from the class dependency graph, including metrics derived from Social Network Analysis. The application development evolved through phases, characterized by a different level of adoption of some key agile practices — namely pair programming, test-based development and refactoring. The evolution of the metrics of the system, and their behavior related to the agile practices adoption level, is presented and discussed. We show that, in the reported case study, a few metrics are enough to characterize with high significance the various phases of the project. Consequently, software quality, as measured using these metrics, seems directly related to agile practices adoption.

Software Quality Measurement

2019 ◽  
pp. 150-181
Author(s):  
Dalila Amara ◽  
Latifa Ben Arfa Rabai
Keyword(s):  
Literature Review ◽  
Comparative Study ◽  
Quality Assessment ◽  
Software Quality ◽  
Software Metrics ◽  
Development Process ◽  
Quality Measurement ◽  
Object Oriented ◽  
Software Measurement ◽  
Software Quality Measurement

Software measurement helps to quantify the quality and the effectiveness of software to find areas of improvement and to provide information needed to make appropriate decisions. In the recent studies, software metrics are widely used for quality assessment. These metrics are divided into two categories: syntactic and semantic. A literature review shows that syntactic ones are widely discussed and are generally used to measure software internal attributes like complexity. It also shows a lack of studies that focus on measuring external attributes like using internal ones. This chapter presents a thorough analysis of most quality measurement concepts. Moreover, it makes a comparative study of object-oriented syntactic metrics to identify their effectiveness for quality assessment and in which phase of the development process these metrics may be used. As reliability is an external attribute, it cannot be measured directly. In this chapter, the authors discuss how reliability can be measured using its correlation with syntactic metrics.

Fuzzy Logic Classifiers and Models in Quantitative Software Engineering

2009 ◽  
pp. 3142-3159 ◽  
Author(s):  
Witold Pedrycz ◽  
Giancarlo Succi
Keyword(s):  
Software Engineering ◽  
Software Quality ◽  
Software Metrics ◽  
Experimental Studies ◽  
Genetic Optimization ◽  
Learning Abilities ◽  
Optimization Framework ◽  
Logic Models ◽  
Software Modules ◽  
Insight Into

The learning abilities and high transparency are the two important and highly desirable features of any model of software quality. The transparency and user-centricity of quantitative models of software engineering are of paramount relevancy as they help us gain a better and more comprehensive insight into the revealed relationships characteristic to software quality and software processes. In this study, we are concerned with logic-driven architectures of logic models based on fuzzy multiplexers (fMUXs). Those constructs exhibit a clear and modular topology whose interpretation gives rise to a collection of straightforward logic expressions. The design of the logic models is based on the genetic optimization and genetic algorithms, in particular. Through the prudent usage of this optimization framework, we address the issues of structural and parametric optimization of the logic models. Experimental studies exploit software data that relates software metrics (measures) to the number of modifications made to software modules.

Quality Assessment of Standard and Customized COTS Products

2020 ◽  
Vol 11 (3) ◽  
pp. 1-13
Author(s):  
Sudhaman Parthasarathy ◽  
C. Sridharan ◽  
Thangavel Chandrakumar ◽  
S. Sridevi
Keyword(s):  
Software Quality ◽  
Enterprise Resource Planning ◽  
Software Metrics ◽  
Resource Planning ◽  
Quality Attributes ◽  
Future Research ◽  
Quality Model ◽  
Erp System ◽  
Cots Products ◽  
Commercial Off The Shelf

Software quality is a very important aspect in evolving strategy for IT vendors involved in commercial off-the-shelf (COTS) (also referred as packaged software) product development. Software metrics are widely accepted measures for monitoring and managing the quality in software projects. Enterprise resource planning (ERP) systems are COTS products and attempt to integrate data and processes in organizations and often require extensive customization. Using software quality metrics already established in literature, software quality attributes defined by the quality model ISO/IEC 9126 were evaluated for a standard and a customized ERP product. This will help the ERP team to identify the specific quality attributes that were affected owing to customization. This research study infers that there exists a considerable impact of ERP system customization over the quality of ERP product. The implications of the findings for both practice and research are discussed, and possible areas of future research are identified.

On the Stability of Feature Selection Methods in Software Quality Prediction: An Empirical Investigation

2015 ◽  
Vol 25 (09n10) ◽  
pp. 1467-1490 ◽  
Author(s):  
Huanjing Wang ◽  
Taghi M. Khoshgoftaar ◽  
Naeem Seliya
Keyword(s):  
Feature Selection ◽  
Software Quality ◽  
Software Metrics ◽  
Subset Selection ◽  
Feature Subset ◽  
Selection Methods ◽  
Statistical Measures ◽  
Quality Modeling ◽  
The Stability ◽  
Stable Feature

Software quality modeling is the process of using software metrics from previous iterations of development to locate potentially faulty modules in current under-development code. This has become an important part of the software development process, allowing practitioners to focus development efforts where they are most needed. One difficulty encountered in software quality modeling is the problem of high dimensionality, where the number of available software metrics is too large for a classifier to work well. In this case, many of the metrics may be redundant or irrelevant to defect prediction results, thereby selecting a subset of software metrics that are the best predictors becomes important. This process is called feature (metric) selection. There are three major forms of feature selection: filter-based feature rankers, which uses statistical measures to assign a score to each feature and present the user with a ranked list; filter-based feature subset evaluation, which uses statistical measures on feature subsets to find the best feature subset; and wrapper-based subset selection, which builds classification models using different subsets to find the one which maximizes performance. Software practitioners are interested in which feature selection methods are best at providing the most stable feature subset in the face of changes to the data (here, the addition or removal of instances). In this study we select feature subsets using fifteen feature selection methods and then use our newly proposed Average Pairwise Tanimoto Index (APTI) to evaluate the stability of the feature selection methods. We evaluate the stability of feature selection methods on a pair of subsamples generated by our fixed-overlap partitions algorithm. Four different levels of overlap are considered in this study. 13 software metric datasets from two real-world software projects are used in this study. Results demonstrate that ReliefF (RF) is the most stable feature selection method and wrapper based feature subset selection shows least stability. In addition, as the overlap of partitions increased, the stability of the feature selection strategies increased.

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.

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