Estimation and Evaluation of Software Quality at a Particular Stage of Software Development

2017 ◽  
pp. 79-102
Keyword(s):  
Software Development ◽  
Software Quality ◽  
Present Status ◽  
Average Quality ◽  
Quality Estimation ◽  
Conversion Method ◽  
New Ideas ◽  
Future Plans

In this chapter, some new ideas about estimation and evaluation of the quality of software. At the outset, it deals with the possibilities of using a standard conversion method so that lines of code from any language may be compared and be used as a uniform metric. The present work is also credited through the introduction of some new terms like efficiency and variation to understand the change in software quality. The main focus is to evaluate and estimate software quality at a particular stage of software development. This is not average quality understanding, but quality estimation at a particular instance. One of the salient aspects of the method suggested is that the developer can evaluate the work at any stage using the methods given to review the present status and make future plans to meet the required target.

Research and Design of Software Quality Control System Based on the CMMI System

2012 ◽  
Vol 241-244 ◽  
pp. 2837-2840
Author(s):  
Ying Lan Fang ◽  
Bing Han ◽  
Wei Fu Zhou
Keyword(s):  
Quality Assurance ◽  
Software Development ◽  
Software Quality ◽  
Process Management ◽  
Design Development ◽  
Information Industry ◽  
New Ideas ◽  
Development Methods ◽  
Research And Design ◽  
Correct Implementation

In recent years, the "software crisis" in the information industry which reflected the traditional software development methods had no longer met the current software quality assurance. In order to solve the outstanding problems, this paper continuous explores and in-depth studies CMMI system. It makes engineering idea apply to complex software development process management and quality assurance, uses new ideas and methods to achieve efficient control with multi-dimensional and multi-level heterogeneous characteristics model system for software engineering quality to ensure the correct implementation in the process throughout the organization. It provides reasonable technical support so as to achieve the improvement of software development efficiency and quality. Contrast to the popular defect management software, this system innovatively achieves multi-dimensional systematic management from requirements, design, development, defects, testing, evaluation and dissemination with a certain degree of practicality, feasibility and promotion.

scholarly journals Object oriented quality prediction through artificial intelligence and machine learning: a survey

2020 ◽  
Vol 5 (17) ◽  
pp. 1-5
Author(s):  
Jitendrea Kumar Saha ◽  
Kailash Patidar ◽  
Rishi Kushwah ◽  
Gaurav Saxena
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Dynamic Behavior ◽  
Software Quality ◽  
Object Oriented ◽  
Quality Metrics ◽  
Quality Prediction ◽  
Quality Estimation ◽  
Future Directions

Software quality estimation is an important aspect as it eliminates design and code defects. Object- oriented quality metrics prediction can help in the estimation of software quality of any defects and the chances of errors. In this paper a survey and the case analytics have been presented for the object-oriented quality prediction. It shows the analytical and experimental aspects of previous methodologies. This survey also elaborates different object-oriented parameters which is useful for the same problem. It also elaborates the problem aspects as well the limitations for the future directions. Machine learning and artificial intelligence methods have been considered mostly for this survey. The parameters considered are inheritance, dynamic behavior, encapsulation, objects etc.

scholarly journals Quality in Software Development: a pragmatic approach using metrics

2014 ◽  
Vol 52 ◽  
Author(s):  
Daniel Acton ◽  
Derrick Kourie ◽  
Bruce Watson
Keyword(s):  
Software Development ◽  
Software Quality ◽  
Software Projects ◽  
New Approach ◽  
Software Products ◽  
Main Disadvantage ◽  
Insight Into ◽  
Gaining Insight

As long as software has been produced, there have been efforts to strive for quality in software products. In order to understand quality in software products, researchers have built models of software quality that rely on metrics in an attempt to provide a quantitative view of software quality. The aim of these models is to provide software producers with the capability to define and evaluate metrics related to quality and use these metrics to improve the quality of the software they produce over time. The main disadvantage of these models is that they require effort and resources to define and evaluate metrics from software projects. This article briefly describes some prominent models of software quality in the literature and continues to describe a new approach to gaining insight into quality in software development projects. A case study based on this new approach is described and results from the case study are discussed.

Improving software quality using Six Sigma DMAIC-based approach: a case study

2017 ◽  
Vol 23 (4) ◽  
pp. 842-856 ◽  
Author(s):  
Racha Karout ◽  
Anjali Awasthi
Keyword(s):  
Software Development ◽  
Software Quality ◽  
Six Sigma ◽  
Content Type ◽  
Cycle Times ◽  
Software Products ◽  
Software Applications ◽  
Quality Aspects ◽  
Culture Of Quality

Purpose Managing quality is a vital aspect in software development world, especially in the current business competition for the fast delivery of feature rich products with high quality. For an organization to meet its intended level of excellence in order to ensure its success, a culture of quality should be built where every individual is responsible of quality and not just the software testing team. However, delivering software products with very few bugs is a challenging constraint that is usually sacrificed in order for a company to meet other management constraints such as cost, scope and scheduling. The paper aims to discuss these issues. Design/methodology/approach In this paper, the authors present a Six Sigma DMAIC-based framework for improving software quality. Different phases of DMAIC methodology are applied for quality improvement in one of the largest software applications for “RK” company (name anonymized) in Canada where critical to quality aspects are identified, production bugs classified and measured, the causes of the large number of production bugs were specified leading to different improvement suggestions. Several metrics were proposed to help “RK” company control its software development process to ensure the success of the project under study. Findings This paper shows how companies can use a systematic approach such as DMAIC to eliminate errors and improve efficiency. It helps them to identify and implement improvements that leads to an increased confidence in the quality of the product produced at all levels. Originality/value By applying DMAIC at “RK” company the authors were able to demonstrate how DMAIC can help organizations improve the quality of their software products. As a result, reduce cost and cycle times, achieve customer satisfaction and improve profit margin.

scholarly journals Improving bioinformatics software quality through incorporation of software engineering practices

2022 ◽  
Vol 8 ◽  
pp. e839
Author(s):  
Adeeb Noor
Keyword(s):  
Software Engineering ◽  
Software Development ◽  
Software Quality ◽  
Formal Education ◽  
Scientific Software ◽  
Cultural Changes ◽  
Bioinformatics Software ◽  
Software Engineers ◽  
Engineering Practices

Background Bioinformatics software is developed for collecting, analyzing, integrating, and interpreting life science datasets that are often enormous. Bioinformatics engineers often lack the software engineering skills necessary for developing robust, maintainable, reusable software. This study presents review and discussion of the findings and efforts made to improve the quality of bioinformatics software. Methodology A systematic review was conducted of related literature that identifies core software engineering concepts for improving bioinformatics software development: requirements gathering, documentation, testing, and integration. The findings are presented with the aim of illuminating trends within the research that could lead to viable solutions to the struggles faced by bioinformatics engineers when developing scientific software. Results The findings suggest that bioinformatics engineers could significantly benefit from the incorporation of software engineering principles into their development efforts. This leads to suggestion of both cultural changes within bioinformatics research communities as well as adoption of software engineering disciplines into the formal education of bioinformatics engineers. Open management of scientific bioinformatics development projects can result in improved software quality through collaboration amongst both bioinformatics engineers and software engineers. Conclusions While strides have been made both in identification and solution of issues of particular import to bioinformatics software development, there is still room for improvement in terms of shifts in both the formal education of bioinformatics engineers as well as the culture and approaches of managing scientific bioinformatics research and development efforts.

Managing the Quality of UML Models in Practice

2009 ◽  
pp. 1-36 ◽  
Author(s):  
Ariadi Nugroho
Keyword(s):  
Software Development ◽  
Software Quality ◽  
Model Quality ◽  
Model Driven ◽  
Testing Phase ◽  
Development Life Cycle ◽  
Computer Scientists ◽  
Model Driven Software Development ◽  
Quality Assessments

Many studies have been carried out to investigate what makes up good quality software. Some of the early models that define the quality of software come from Boehm (1976) and McCall (1977). Works in this field of quality models have traditionally focused on quality of the final software product. Since the 1970’s models of software have been used and this has recently attracted much attention through the popularity of model-driven software development (MDSD). However, quality of software models has rarely been considered (Lange & Chaudron, 2005). In the software development life cycle, the ability to assure software quality long before the testing phase may save a lot of money since less defects found in the testing phase will mean less effort to be allocated for rework. Currently, the importance of model quality is starting to gain attention from computer scientists. Work in this area has since focused on developing tools, metrics, and frameworks to improve the quality of models that guide implementation, particularly in the context of UML modeling which has become the de facto standard for building object oriented software. Quality of models can be considered from many different perspectives. In this chapter, we will consider the following perspectives: Firstly, is the model complete in the sense that it describes the information that developers need to know about a system? Secondly, we look at the degree in which a model of a system and an implementation correspond. This degree of correspondence indicates to what extent analyses of—or predictions based on the model are valid for the implementation. We present the main findings from case studies into quality of modeling in the software industry as well as findings from a survey amongst professional software developers. We also provide a discussion on the contemporary methods for design quality assessments.

A Survey on Quality Attributes and Quality Models for Embedded Software

2021 ◽  
pp. 1114-1132
Author(s):  
Zouheyr Tamrabet ◽  
Toufik Marir ◽  
Farid MOKHATI
Keyword(s):  
Embedded Systems ◽  
Software Development ◽  
Software Quality ◽  
Embedded Software ◽  
Entire System ◽  
Quality Engineering ◽  
High Quality ◽  
Quality Models ◽  
Software Failures

This article describes how software quality engineering is an inevitable activity, which must be accomplished during software development process in order to avoid software failures and ensuring its quality. Embedded systems are computer platforms, which require high quality software. Many researchers interested in embedded systems have demonstrated that the quality of the embedded software has a significant effect on the performances of the entire system. In the literature, several works have been emerged from this line of research. The aim of this article is to present a survey of the most important works, which deal with embedded software quality engineering. A comparative study is also given in order to show strengths and weaknesses of each work.

Comparing Feature Selection Techniques for Software Quality Estimation Using Data-Sampling-Based Boosting Algorithms

2015 ◽  
Vol 22 (03) ◽  
pp. 1550013
Author(s):  
Taghi M. Khoshgoftaar ◽  
Kehan Gao ◽  
Ye Chen ◽  
Amri Napolitano
Keyword(s):  
Feature Selection ◽  
Software Quality ◽  
Prediction Models ◽  
Class Imbalance ◽  
Data Sampling ◽  
Classification Models ◽  
Quality Estimation ◽  
Boosting Algorithm ◽  
The Impact

Software defect prediction is a classification technique that utilizes software metrics and fault data collected during the software development process to identify fault-prone modules before the testing phase. It aims to optimize project resource allocation and eventually improve the quality of software products. However, two factors, high dimensionality and class imbalance, may cause low quality training data and subsequently degrade classification models. Feature (software metric) selection and data sampling are frequently used to overcome these problems. Feature selection (FS) is a process of choosing a subset of relevant features so that the quality of prediction models can be maintained or improved. Data sampling alters the dataset to change its balance level, therefore alleviating the problem of traditional classification models that are biased toward the overrepresented (majority) class. A recent study shows that another method, called boosting (building multiple models, with each model tuned to work better on instances misclassified by previous models), is also effective for addressing the class imbalance problem. In this paper, we present a technique that uses FS followed by a boosting algorithm in the context of software quality estimation. We investigate four FS approaches: individual FS, repetitive sampled FS, sampled ensemble FS, and repetitive sampled ensemble FS, and study the impact of the four approaches on the quality of the prediction models. Ten base feature ranking techniques are examined in the case study. We also employ the boosting algorithm to construct classification models with no FS and use the results as the baseline for further comparison. The empirical results demonstrate that (1) FS is important and necessary prior to the learning process; (2) the repetitive sampled FS method generally has similar performance to the individual FS technique; and (3) the ensemble filter (including sampled ensemble filter and repetitive sampled ensemble filter) performs better than or similarly to the average of the corresponding individual base rankers.

Kualitas Hidup Pasien Tuberkulosis Paru yang Mengalami Stigma Diri (Quality of Life Pulmonary Tuberculosis Patients With Self Stigma))

2019 ◽  
Vol 3 (2) ◽  
pp. 17-27
Author(s):  
Yunita Sari
Keyword(s):  
Quality Of Life ◽  
Pulmonary Tuberculosis ◽  
Descriptive Study ◽  
Negative Influence ◽  
Questionnaire Data ◽  
Average Quality ◽  
Tuberculosis Patients ◽  
Pulmonary Tb ◽  
Life Score

Pulmonary tuberculosis (TB) is a chronic disease that can bring about the sufferer's self-stigma and also affect his quality of life. A number of studies report that living with TB has a negative influence on the quality of life of sufferers even with or without self-stigma. The purpose of this study was to identify the quality of life of TB patients who experienced self-stigma. This research is a descriptive study, sample were 31 pulmonary TB patients. Data was collected using a questionnaire. Data analyzed by using frequency distribution and percentage. The researcher first screened TB patients who experienced self-stigma. The results showed that 25 people (80.64%) respondents experienced mild self-stigma. A total of 9 respondents (36%) had a quality of life score in the good category and as many as 16 respondents (64%) had enough category with an average quality of life score is 56.57. While respondents who had moderate self-stigma were 6 people (19.36%) with a good quality of life score was 1 person (16.67%) and enough category quality of life score were 5 people (83.33%) with an average quality of life score is 49.92.

Why Is Safe Science Good Science?

2018 ◽  
Author(s):  
Camilla Kao ◽  
Che-I Kao ◽  
Russell Furr
Keyword(s):  
Decision Making ◽  
Design Thinking ◽  
Good Science ◽  
Safety Issues ◽  
Safety Requirements ◽  
The Arts ◽  
Hands On ◽  
Obvious Benefit ◽  
New Ideas

In science, safety can seem unfashionable. Satisfying safety requirements can slow the pace of research, make it cumbersome, or cost significant amounts of money. The logic of rules can seem unclear. Compliance can feel like a negative incentive. So besides the obvious benefit that safety keeps one safe, why do some scientists preach "safe science is good science"? Understanding the principles that underlie this maxim might help to create a strong positive incentive to incorporate safety into the pursuit of groundbreaking science.<div><br></div><div>This essay explains how safety can enhance the quality of an experiment and promote innovation in one's research. Being safe induces a researcher to have <b>greater control</b> over an experiment, which reduces the <b>uncertainty</b> that characterizes the experiment. Less uncertainty increases both <b>safety</b> and the <b>quality</b> of the experiment, the latter including <b>statistical quality</b> (reproducibility, sensitivity, etc.) and <b>countless other properties</b> (yield, purity, cost, etc.). Like prototyping in design thinking and working under the constraint of creative limitation in the arts, <b>considering safety issues</b> is a hands-on activity that involves <b>decision-making</b>. Making decisions leads to new ideas, which spawns <b>innovation</b>.</div>

Sign in / Sign up

Export Citation Format

Share Document