scholarly journals Methods of determination of quality of software

2020 ◽  
Vol 30 (1) ◽  
pp. 158-167
Author(s):  
Ю. І. Грицюк ◽  
Т. О. Муха
Keyword(s):  
Software Metrics ◽  
Software Tool ◽  
Assessment Process ◽  
Software Project ◽  
Software Projects ◽  
Strategic Objectives ◽  
Metric Analysis ◽  
Software Product

Developed modern software tool for determining the quality of software (SW) techniques metric analysis. The software allows you to use quality metrics to calculate the corresponding metric and determine the value of the complex index of quality software product. Clarified the quality assessment process, software analyzes the concept of the quality of the software product as an object of standardization and quality levels of performance models of the software. This allowed the opportunity to improve the quality of software by generating the relevant requirements of the criteria for quality evaluation. It is also possible to make the improvement of the metric analysis of models of its quality and its quantitative measurement methods in all phases of a software project. It was revealed that the driving force behind the success of software projects is the desire of their leaders to develop such software, which would have a certain value. It should be important for certain tasks or to achieve tactical and strategic objectives. The value of the software can be expressed in the form of its value, or in some other form. The customer usually has their own idea of ​​the maximum cost of investing in the development of software. These funds profit it expects to achieve in the case of the main goals of using the software. It can also have a vision of the functionality of software and certain expectations of its quality. The features of the use of the metric analysis for determining the quality of the software, revealed the lack of uniform standards for the metric. Therefore, each supplier of its measurement system offers its own methods of evaluating the quality of software and associated metrics. Also it is challenging the interpretation of metric values, since for the majority of users of its software metrics and their values ​​are not absolutely clear and informative. It was found that the main parameters of the choice of an embodiment of the software is its cost, the duration of the development process and the reputation of the designer of the company. But the decisions taken on the basis of these parameters, not always guarantee proper quality of the software.

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.

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.

Usability evaluation of component based software system using software metrics

2020 ◽  
Vol 14 (3) ◽  
pp. 281-289
Author(s):  
Jyoti Agarwal ◽  
Sanjay Kumar Dubey ◽  
Rajdev Tiwari
Keyword(s):  
Software Development ◽  
Software Metrics ◽  
Research Work ◽  
Fuzzy Model ◽  
Software Project ◽  
Software System ◽  
Quality Factors ◽  
Quality Product ◽  
Software Product ◽  
Component Based Software Engineering

Component Based Software Engineering (CBSE) provides a way to create a new Component Based Software System (CBSS) by utilizing the existing components. The primary reason for that is to minimize the software development time, cost and effort. CBSS also increases the component reusability. Due to these advantages, software industries are working on CBSS and continuously trying to provide quality product. Usability is one of the major quality factors for CBSS. It should be measured before delivering the software product to the customer, so that if there are any usability flaws, it can be removed by software development team. In this paper, work has been done to evaluate the usability of CBSS based on major usability sub-factors (learnability, operability, understandability and configurability). For this purpose, firstly software metrics are identified for each usability sub-factor and the value of each sub-factor is evaluated for a component based software project. Secondly, overall usability of the software project is evaluated by using the calculated value of each usability sub-factor. Usability for the same project was also evaluated using Fuzzy approach in MATLAB to validate the experimental work of this research paper. It was identified that the value of usability obtained from software metrics and fuzzy model was very similar. This research work will be useful for the software developer to evaluate the usability of any CBSS and will also help them to compare different version of any CBSS in term of their usability.

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.

Automatic, Task-Sensitive and Simulation-Based Optimization of Fringe Projection Measurements

2010 ◽  
Vol 437 ◽  
pp. 439-443 ◽  
Author(s):  
Johannes Weickmann ◽  
Albert Weckenmann ◽  
Peter Frederik Brenner
Keyword(s):  
Quality Control ◽  
Software Tool ◽  
Continuous Model ◽  
Fringe Projection ◽  
Measurement Result ◽  
Positioning Strategy ◽  
Methodical Approach ◽  
Simulation Based

Fringe projection systems gain in importance in manufacturing quality control due to their multiple advantages. However, determination of an optimal inspection setup – including sighting- and positioning-strategy and configuration of the sensor – is a challenging task. There is no standardized, methodical approach established yet. Thus the success of an inspection depends on the skills and diligence of the inspection planner. This paper shows an approach for the reduction of these shortfalls. Therefore a software-tool was developed, which provides a simulation-based, task-sensitive and automatic optimization of inspection setups. Fundament for the simulation of the measurement is a continuous model. Thus the measurement result and the quality of a chosen inspection setup can be simulated. Based on this, algorithms for the multi-criteria-optimization of inspection setups were implemented.

scholarly journals PRACTICAL QUALITY ANALYSIS OF OPEN SOURCE SOFTWARE

2020 ◽  
pp. 49-55
Author(s):  
Yevheniia Kataieva ◽  
Svetlana Odokienko ◽  
Maya Luta ◽  
Yaroslav Savchenko
Keyword(s):  
Software Quality ◽  
Software Metrics ◽  
Quality Analysis ◽  
Quality Model ◽  
Complex Concept ◽  
Software Complexity ◽  
Complexity Metrics ◽  
Software Product ◽  
High Level

The success of any project is determined by its ability to meet the needs of the consumer, and therefore ensuring a high level of quality is a necessary task of any production, including software engineering. Insufficient quality of the created software requires many IT-organizations, up to 70% of the budget of the information system to reserve for the maintenance stage, with up to 60% of all software modifications performed to eliminate errors, and only the remaining 40% - to correct software within the business process, improvement certain indicators of software quality, or to prevent potential problems. Software quality is a complex concept. Standards highlight the quality of development processes, internal and external quality of the software product, the quality of the software product at the stage of use. For each of the components of quality can be called a set of metrics that determine the quality of the software product. The resulting structure is called the software quality model. Software metrics are a measure that allows you to get the numerical value of a property of software or its specifications, as well as the method of its calculation. Metrics allow you to get numeric values for each property of the software or its specifications. Of particular interest are software complexity metrics. Complexity is an important factor on which other parameters of software quality depend, such as accuracy, correctness, reliability, convenience of support. The existence of methods and algorithms for automatic calculation of software complexity metrics using software allows you to get a comprehensive formal report on the quality of software in a short time. This allows for objective monitoring of the quality of software throughout the project life cycle, make adjustments to the project plan, as well as make timely decisions about the need for refactoring.

scholarly journals The Quality of Software Metrics

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dragoljub Pilipović ◽  
Dejan Simeunović
Keyword(s):  
Software Development ◽  
Software Metrics ◽  
Software Tool

This paper discusses the definition, types, characteristic and construction of software metrics in the field of software development. Finally, an overview is given regarding the use of a software tool in software development in relation to software metrics in the field of banking.

Fixing the Bugs in Software Projects from Software Repositories for Improvisation of Quality

2020 ◽  
Vol 13 (2) ◽  
pp. 184-192 ◽  
Author(s):  
Ekbal Rashid ◽  
Mohd D. Ansari
Keyword(s):  
Software Project ◽  
Software Projects ◽  
Software Repositories ◽  
Bug Fixing ◽  
Closing Rate

Background: The study of bugs that are reported and close may indicate the growth and working of a software project. It may also indicate the quality of the project. Methods: As software projects grow, the number of bugs reported generally increases each year. To maintain quality, the developers have to resolve and close these increasing numbers of bugs. Results: The present paper discusses the relations between bugs being reported and bugs being closed. It also discusses some parameters related to the study of bugs. In this paper, new parameters have been introduced that help in the improvement of quality and this is the novelty of paper. Conclusion: The research mainly covers the problem of finding the relation of collaborative growth with the quality of software. The paper also covers improvements in parameters like the rate of bug fixing. It also discusses the significance of these parameters. We have suggested a new parameter called the bug closing rate. And this can be calculated in two ways.

scholarly journals Development and Application of New Quality Model for Software Projects

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
K. Karnavel ◽  
R. Dillibabu
Keyword(s):  
Software Testing ◽  
Computation Time ◽  
Time Cost ◽  
Quality Model ◽  
Software Projects ◽  
It Industry ◽  
Software Product ◽  
The Cost ◽  
Very High

The IT industry tries to employ a number of models to identify the defects in the construction of software projects. In this paper, we present COQUALMO and its limitations and aim to increase the quality without increasing the cost and time. The computation time, cost, and effort to predict the residual defects are very high; this was overcome by developing an appropriate new quality model named the software testing defect corrective model (STDCM). The STDCM was used to estimate the number of remaining residual defects in the software product; a few assumptions and the detailed steps of the STDCM are highlighted. The application of the STDCM is explored in software projects. The implementation of the model is validated using statistical inference, which shows there is a significant improvement in the quality of the software projects.

scholarly journals Early Prediction of a Team Performance in the Initial Assessment Phases of a Software Project for Sustainable Software Engineering Education

2020 ◽  
Vol 12 (11) ◽  
pp. 4663 ◽  
Author(s):  
Mehwish Naseer ◽  
Wu Zhang ◽  
Wenhao Zhu
Keyword(s):  
Logistic Regression ◽  
Software Engineering ◽  
Information Gain ◽  
Hybrid Approach ◽  
Initial Assessment ◽  
Engineering Practice ◽  
Software Project ◽  
Early Assessment ◽  
Software Projects ◽  
Software Product

Software engineering is a competitive field in education and practice. Software projects are key elements of software engineering courses. Software projects feature a fusion of process and product. The process reflects the methodology of performing the overall software engineering practice. The software product is the final product produced by applying the process. Like any other academic domain, an early evaluation of the software product being developed is vital to identify the at-risk teams for sustainable education in software engineering. Guidance and instructor attention can help overcome the confusion and difficulties of low performing teams. This study proposed a hybrid approach of information gain feature selection with a J48 decision tree to predict the earliest possible phase for final performance prediction. The proposed technique was compared with the state-of-the-art machine learning (ML) classifiers, naïve Bayes (NB), artificial neural network (ANN), logistic regression (LR), simple logistic regression (SLR), repeated incremental pruning to produce error reduction (RIPPER), and sequential minimal optimization (SMO). The goal of this process is to predict the teams expected to obtain a below-average grade in software product development. The proposed technique outperforms others in the prediction of low performing teams at an early assessment stage. The proposed J48-based technique outperforms others by making 89% correct predictions.

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