Software Metrics and the Quality of Telecommunication Software

1992 ◽  
pp. 255-266
Author(s):  
Taghi M. Khoshgoftaar ◽  
John C. Munson
Keyword(s):  
Software Metrics

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.

scholarly journals EVALUATION OF SOFTWARE METRICS FOR SOFTWARE PROJECTS

2015 ◽  
Vol 14 (6) ◽  
pp. 5845-5853
Author(s):  
Kunal Chopra ◽  
Monika Sachdeva
Keyword(s):  
Software Metrics ◽  
Control Flow ◽  
Software Development Process ◽  
Software Projects ◽  
Flexible Tool ◽  
Product Metrics ◽  
Software Productivity ◽  
Software Product ◽  
The Many

Software metrics are developed and used by the many software organizations for the evaluation and confirmation of good code, working and maintenance of the software product. Software metrics measure and identify various types of software complexities such as size metrics, control flow metrics and data flow metrics. One of the significant objective of software metrics is that it is applicable to both a process and product metrics. Ndepend is the most advanced as well as flexible tool available in the market. We have ensured the Quality of the project by using Ndepend metrics. So we have concluded that software metrics are easy to understand and applicable on the software, so favourable among software professionals.It is most prevalent and important testing metrics used in organizations. Metrics are used to improve software productivity and quality. This thesis introduces the most commonly used software metrics proposed and reviews their use in constructing models of the software development process.

Software Requirements Risk and Maintainability

2005 ◽  
pp. 2562-2566
Author(s):  
Norman F. Schneidewind
Keyword(s):  
Risk Factors ◽  
Life Cycle ◽  
Software Metrics ◽  
Space Shuttle ◽  
Software Requirements ◽  
Requirements Specifications ◽  
Requirements Change ◽  
Code Metrics ◽  
Change Requests

In order to continue to make progress in software measurement, as it pertains to reliability and maintainability, there must be a shift in emphasis from design and code metrics to metrics that characterize the risk of making requirements changes. By doing so, the quality of delivered software can be improved because defects related to problems in requirements specifications will be identified early in the life cycle. An approach is described for identifying requirements change risk factors as predictors of reliability and maintainability problems. This approach can be generalized to other applications with numerical results that would vary according to application. An example is provided that consists of 24 space shuttle change requests, 19 risk factors, and the associated failures and software metrics.

Measurement in Software Engineering

2020 ◽  
pp. 166-182
Author(s):  
Ruya Samli ◽  
Zeynep Behrin Güven Aydın ◽  
Uğur Osman Yücel
Keyword(s):  
Software Engineering ◽  
Software Testing ◽  
Software Metrics ◽  
Basic Process ◽  
Testing Tools ◽  
Software Metric ◽  
Development Life Cycle ◽  
Metric Set ◽  
Software Development Life Cycle

Measurement in software is a basic process in all parts of the software development life cycle because it helps to express the quality of a software. But in software engineering, measurement is difficult and not precise. However, researchers accept that any measure is better than zero measure. In this chapter, the software metrics are explained, and some software testing tools are introduced. The software metric sets of Chidamber and Kemerer Metric Set (CK Metric Set), MOOD Metric Set (Brito e Abreu Metric Set), QMOOD Metric Set (Bansiya and Davis Software Metric Set), Rosenberg and Hyatt Metric Set, Lorenz and Kidd Metric Set (L&K Metric Set) are explained. The software testing tools such as Understand, Sonargraph, Findbugs, Metrics, PMD, Coverlipse, Checkstyle, SDMetrics, and Coverity are introduced. Also, 17 literature studies are summarized.

SOFTWARE REUSABILITY MODEL FOR PROCEDURE BASED DOMAIN-SPECIFIC SOFTWARE COMPONENTS

2008 ◽  
Vol 18 (08) ◽  
pp. 1063-1081 ◽  
Author(s):  
PARVINDER SINGH SANDHU ◽  
HARDEEP SINGH
Keyword(s):  
Software Metrics ◽  
Semantic Analysis ◽  
Research Work ◽  
Matrix Decomposition ◽  
Probabilistic Latent Semantic Analysis ◽  
Rule Base ◽  
Software Components ◽  
Reusable Components ◽  
Taguchi Analysis

Automatic reusability appraisal is helpful in evaluating the quality of developed or developing reusable software components and in identification of reusable components from existing legacy systems; that can save cost of developing the software from scratch. But the issue of how to identify reusable components from existing systems has remained relatively unexplored. In this paper, we mention a two-tier approach by studying the structural attributes as well as usability or relevancy of the component to a particular domain. We evaluate Probabilistic Latent Semantic Analysis (PLSA) approach, LSA's Singular Value Decomposition (SVD) technique, LSA's Semi-Discrete Matrix Decomposition (SDD) technique and Naïve Bayes approach to determine the Domain Relevancy of software components. It exploits the fact that Feature Vector codes can be seen as documents containing terms — the identifiers present in the components — and so text modeling methods that capture co-occurrence information in low-dimensional spaces can be used. In this research work, structural attributes of software components are explored using software metrics and quality of the software is inferred by Neuro-Fuzzy (NF) Inference engine, taking the metric values as input. The influence of different factors on the reusability is studied and the condition for the optimum reusability index is derived using Taguchi Analysis. The NF system is optimized by selecting initial rule-base through modified ID3 decision tree algorithm in combination with the results of Taguchi Analysis. The calculated reusability value enables to identify a good quality code automatically. It is found that the reusability value determined is close to the manual analysis used to be performed by the programmers or repository managers. So, the system developed can be used to enhance the productivity and quality of software development.

scholarly journals Performance Analysis of Feature Selection Methods in Software Defect Prediction: A Search Method Approach

2019 ◽  
Vol 9 (13) ◽  
pp. 2764 ◽  
Author(s):  
Abdullateef Oluwagbemiga Balogun ◽  
Shuib Basri ◽  
Said Jadid Abdulkadir ◽  
Ahmad Sobri Hashim
Keyword(s):  
Software Metrics ◽  
Prediction Models ◽  
Predictive Performance ◽  
Search Method ◽  
Feature Subset Selection ◽  
Defect Prediction ◽  
Feature Subset ◽  
Software Defect Prediction ◽  
Software Defect

Software Defect Prediction (SDP) models are built using software metrics derived from software systems. The quality of SDP models depends largely on the quality of software metrics (dataset) used to build the SDP models. High dimensionality is one of the data quality problems that affect the performance of SDP models. Feature selection (FS) is a proven method for addressing the dimensionality problem. However, the choice of FS method for SDP is still a problem, as most of the empirical studies on FS methods for SDP produce contradictory and inconsistent quality outcomes. Those FS methods behave differently due to different underlining computational characteristics. This could be due to the choices of search methods used in FS because the impact of FS depends on the choice of search method. It is hence imperative to comparatively analyze the FS methods performance based on different search methods in SDP. In this paper, four filter feature ranking (FFR) and fourteen filter feature subset selection (FSS) methods were evaluated using four different classifiers over five software defect datasets obtained from the National Aeronautics and Space Administration (NASA) repository. The experimental analysis showed that the application of FS improves the predictive performance of classifiers and the performance of FS methods can vary across datasets and classifiers. In the FFR methods, Information Gain demonstrated the greatest improvements in the performance of the prediction models. In FSS methods, Consistency Feature Subset Selection based on Best First Search had the best influence on the prediction models. However, prediction models based on FFR proved to be more stable than those based on FSS methods. Hence, we conclude that FS methods improve the performance of SDP models, and that there is no single best FS method, as their performance varied according to datasets and the choice of the prediction model. However, we recommend the use of FFR methods as the prediction models based on FFR are more stable in terms of predictive performance.

Software Metrics for Computing Quality of Software Agents

2020 ◽  
Vol 17 (5) ◽  
pp. 2035-2038
Author(s):  
E. Ajith Jubilson ◽  
Ravi Sankar Sangam
Keyword(s):  
Software Metrics ◽  
Online Shopping ◽  
Software Agents ◽  
Evaluation Process ◽  
Building Blocks ◽  
Software Agent ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

Metrics are the essential building blocks for any evaluation process. They establish specific goals for improvement. Multi agent system (MAS) is complex in nature, due to the increase in complexity of developing a multi agent system, the existing metrics are less sufficient for evaluating the quality of an MAS. This is due to the fact that agent react in an unpredictable manner. Existing metrics for measuring MAS quality fails to addresses potential communication, initiative behaviour and learn-ability. In this work we have proposed additional metrics for measuring the software agent. A software agent for online shopping system is developed and the metrics values are obtained from it and the quality of the multi agent system is analysed.

scholarly journals Performance Evaluation of Pseudo Code with Weka for Accuracy Calculation

2019 ◽  
Vol 8 (4) ◽  
pp. 7818-7823
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Data Transmission ◽  
Software Metrics ◽  
Fault Prediction ◽  
Learning Methods ◽  
Accuracy Measurement ◽  
Machine Learning Methods ◽  
Interoperability Test

Programming testing is a fundamental and essential advance of the existence cycle of programming improvement to recognize and defects in programming and afterward fix the deficiencies. The reliability of the data transmission or the quality of proper processing ,maintenance and retrieval of information to a server can be tested for some systems. Accuracy is also one factor that is usually used to the Joint Interoperability Test Command as a criterion for accessing interoperability. This is the main investigation of PC flaw forecast and exactness as per our examination, which spotlights on the utilization of PROMISE database dataset. Some PROMISE database dataset tests are compared between pseudo code (PYTHON) and actual software (WEKA),which in computer fault prediction and accuracy measurement are effective software metrics and machine learning methods.

Use of Software Metrics to Improve the Quality of Software Projects Using Regression Testing

2018 ◽  
pp. 204-218
Author(s):  
Arshpreet Kaur Sidhu ◽  
Sumeet Kaur Sehra
Keyword(s):  
Software Metrics ◽  
Early Stage ◽  
Regression Testing ◽  
Stage Model ◽  
Software Projects ◽  
Model Based ◽  
Model Based Testing ◽  
The Cost ◽  
Structural Metrics

Testing of software is broadly divided into three types i.e., code based, model based and specification based. To find faults at early stage, model based testing can be used in which testing can be started from design phase. Furthermore, in this chapter, to generate new test cases and to ensure the quality of changed software, regression testing is used. Early detection of faults will not only reduce the cost, time and effort of developers but also will help finding risks. We are using structural metrics to check the effect of changes made to software. Finally, the authors suggest identifying metrics and analyze the results using NDepend simulator. If results show deviation from standards then again perform regression testing to improve the quality of software.

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