Automating Software Design Metrics.

Aims: Software quality assurance is a formal process for evaluating and documenting the quality of the work products during each stage of the software development lifecycle.The practice of applying software metrics to operational factors and to maintain factors is a complex task.Successful software quality assurance is highly dependent on software metrics.It needs linkage the software quality model and software metrics through quality factors in order to offer measure method for software quality assurance.The contributions of this paper build an appropriate method of Software quality metrics application in quality life cycle with software quality assurance.Design: The purpose approach defines some software metrics in the factors and discussed several software quality assurance model and some quality factors measure method.Methodology: This paper solves customer value evaluation problem are: Build a framework of combination of software quality criteria.Describes software metrics.Build Software quality metrics application in quality life cycle with software quality assurance.Results: From the appropriate method of Software quality metrics application in quality life cycle with software quality assurance, each activity in the software life cycle, there is one or more QA quality measure metrics focus on ensuring the quality of the process and the resulting product.Future research is need to extend and improve the methodology to extend metrics that have been validated on one project, using our criteria, valid measures of quality on future software project.

Software quality indicates how effective and efficient a software is. Various standards need to be used to evaluate software quality. One of the most important internationally accepted standards for measuring software quality is the ISO/IEC 25010 software quality standard. With this standard, the quality of a software product is evaluated with eight different metrics. These are functional suitability, performance, compatibility, usability, reliability, security, maintainability and portability metrics. In this study, we tried to determine the relationship between user feedback and the metrics of the ISO/IEC 25010 software quality standard. Machine learning (ML) and natural language processing (NLP) techniques were used to classify user comments. After the data preprocessing phase, vectors of user comments were extracted with the Tf-Idf method for NLP. As a machine learning method, classification was made using five different models: Extra Trees Classifier (ETC), Gaussian Process Classifier (GPC), MLP Classifier (MLPC), Bernoulli Naive Bayes Classifier (BNBC) and Support Vector Classifier (SVC). Our goal is to show how quality metrics can be classified into multiple classes using user notifications. The data set used has an unbalanced structure, containing 1681 user comments classified by software experts. Synthetic Minority Oversampling Technique (SMOTE) was used to address the imbalance in the dataset. The results were compared by applying the same classification models to unbalanced and balanced data sets. According to the results obtained, the best classification model is the Extra Trees Classifier model, which provides the highest accuracy rate of 87% according to the SMOTE applied data set. The results show that ML and NLP methods can be used effectively in the classification process of software quality metrics.

The term “software crisis” refers to the huge amount of resources needed for the development and maintenance of software. A major problem with research in these areas is the lack of solid data. In particular, there is little data that can be used to predict the types of problems that are likely to occur during the software's maintenance.This paper describes the results of an analysis of a software system that underwent several revisions. Maintenance of the system was performed by distinct programming teams. At each revision of the software system, the analysis was performed by doing measurements of module complexity based on three quantities: Halstead's Software Science Effort, McCabe's cyclomatic complexity, and coupling analyses. The relationships of Software Science Effort to unchangeability of modules, of cyclomatic complexity to path coverage, and coupling analysis to measurement of modularity and coherence during the maintenance are given.

Leveraging and acting on the results of static analysis is a well-known best practice. Static analysis also is an integral part of software quality assurance which is highlighted by the inclusion of static analysis results in software quality models like ColumbusQM and Quamoco. Although there are studies that explore if tools for static analysis are used and how they are configured, few publications explore the longitudinal effects of acting on static analysis results on software and on the evolution of software. Especially effects on quality criteria, e.g., software quality metrics, defects, or readability are missing. With our research, we will bridge this gap and measure the effects of static analysis on software quality evolution. We will measure the effect the removal of code that generates static analysis warnings has on software quality metrics. Furthermore, we will measure long term effects on external quality attributes, e.g., reported issues and defects. Finally, we want to predict false positives of static analysis warnings by training predictive models on our collected data.

Software engineering is a growing part of computer science. Software engineering attempts to put measurements and metrics into the area of software programming. One of these metrics, Halstead's software science [1], measures a program's difficulty, a language level, and in turn, a programmer's productivity. Software science uses an extensive measure as opposed to the intensive measure of most other software engineering techniques. This is accomplished by counting functions and operands, and applying several formulae modified from thermodynamics. Even though software science attempts to place an objective measurement on these parameters, it is not meant to be a definitive decision tool, but rather a general guideline. This paper will explore the possible extension of Halstead's theory to include operators, as defined in APL, in the counting methodology and software science formulae.

Numerous virtual environments with automatic program evaluation have emerged to assist the teaching-learning process, allowing timely feedback. In a review of these environments, we find few studies that focus on an approach centered on refactoring: where students are strongly encouraged to refactor, improving the submitted code to also meet quality criteria. In a traditional environment, the student submits the answer and if it is dynamically correct, he goes to the next question. In this work, we propose a complementary approach based on software engineering metrics, which allow a finer evaluation of the code where the programmer, after having his dynamically correct answer, is invited and encouraged to refactor his solution towards an optimal code that also meets the software quality metrics. The work is based on source code in the Python language and shows which software quality metrics can be used with the purpose of encouraging students to refactor their code in programming fundamentals disciplines.

This work describes the production of an application Error Propagation Metrics from XMI which can extract process and display software design metrics from XMI files. The tool archives these design metrics in a standard XML format defined by a metric document type definition.;XMI is a flavour of XML allowing the description of UML models. As such, the XMI representation of a software design will include information from which a variety of software design metrics can be extracted. These metrics are potentially useful in improving the software design process, either throughout the early stages of design if a suitable XMI-enabled modelling tool is deployed, or to enable the comparison of completed software projects, by extracting design metrics from UML models reverse engineered from the implemented source code.;The tool is able to derive the error propagation of metrics from test XMI files created from UML sequence and state diagrams and from reverse engineered Java source code. However, variation was observed between the XMI representations generated by different software design tools, limiting the ability of the tool to process XMI from all sources. Furthermore, it was noted that subtle differences between UML design representations might have a marked effect on the quality of metrics derived.;In conclusion in order to validate the usefulness of these metrics that can be extracted from XMI files it would be useful to follow well-documented design projects throughout the total design and implementation process. Alternatively, the tool might be used to compare metrics from well-matched design implementations. In either case design metrics will only be of true value to software engineers if they can be associated empirically with a validated measure of system quality.

We empirically examine the relationship between software design modularity and software quality in open source software (OSS) development projects. Conventional wisdom suggests that degree of software modularity affects software quality. An analysis of 203 software releases in 46 OSS projects hosted on SourceForge.net lends support for a more complex relationship between software modularity and software quality than conventional wisdom suggests. We find that software modularity is associated with reduced software complexity, an increased number of static software bugs, and a mixed relationship with the percentage of bugs closed. We do not find empirical evidence supporting any relationship between modularity and other measures of customer satisfaction. In addition to empirically testing the relationship between modularity and quality, we introduce new measures of software modularity and software quality. Implications are developed for the theory of modularity and the practice of software development.

Source code refactorings and transformations are extensively used by embedded system developers to improve the quality of applications, often supported by various open source and proprietary tools. They either aim at improving the design time quality such as the maintainability and reusability of software artifacts, or the runtime quality such as performance and energy efficiency. However, an inherent trade-off between design- and run-time qualities is often present posing challenges to embedded software development. This work is a first step towards the investigation of the impact of transformations for improving the performance and the energy efficiency on software quality metrics and the impact of refactorings for increasing the design time quality on the execution time, the memory and the energy consumption. Based on a set of embedded applications from widely used benchmark suites and typical transformations and refactorings, we identify interrelations and trade-offs between the aforementioned metrics.

Virtual Reality (VR) Software is becoming more mainstream in recent years. It has provided an opportunity for VR practitioners to explore new domains and deliver cutting edge products. The success of the VR products depends primarily on the product contextual relevance and qualities exhibited. However, it is unclear how VR practitioners curb software quality challenges and improve the essence of the VR product over every release. In this paper, we present a Systematic Mapping Study of the software quality metrics adopted by VR practitioners for assessing the quality of their VR products. The study showed that practitioners used unique metrics to measure the quality of their VR products in addition to adopting some of existing enterprise software metrics. Further, we consolidate these metrics into different themes that future practitioners may use for developing VR products.

Size, complexity, and human dependency on software--based products have grown dramatically during past decades. Software developers are struggling to deliver reliable software with acceptable level of quality, within given budget and schedule. One measure of software quality and reliability is the number of residual faults. Therefore, researchers are focusing on the identification of the number of fault presents in the software or identification of program modules that are most likely to contain faults. A lot of models have been developed using various techniques. A common approach is followed for software reliability prediction utilizing failure data. Software reliability and quality prediction is highly desired by the stakeholders, developers, managers, and end users. Detecting software faults early during development will definitely improve the reliability and quality in cost-effective way.KeywordsSoftware ReliabilityFault PredictionSoftware MetricsCapability Maturity ModelTest Case PrioritizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cost, schedule and quality are highly correlated factors in software development. They basically form three sides of the same triangle. Beyond a certain point (the "Quality is Free" point), it is difficult to increase the quality without increasing either cost or schedule or both for the software under development. As products and applications mature, users expect higher quality products. They want IT organizations to be responsible and accountable for the quality claims made by the product marketing teams. In the last couple decades, much software engineering research has focussed on standards, methodologies and techniques for improving software quality, measuring software quality and software quality assurance. Most of this research is focused on the internal/development view of quality. More recent studies done in conjunction with the marketing groups have made attempts to understand the customer view of quality. All of these different ongoing activities to understand quality from the various perspectives have made the field even more enriching and exciting. The Fifth Workshop on Software Quality aims to bring together academic, industrial and commercial communities interested in software quality topics to discuss the different technologies being defined and used in the software quality area.

: Software metrics (or measurements) which predict software quality have been refined and enhanced. Metrics were classified as anomaly-detecting metrics which identify deficiencies in documentation or source code, predictive metrics which measure the logic of the design and implementation, and acceptance metrics which are applied to the end product to assess compliance with requirements. A Software Quality Measurement Manual was produced which contained procedures and guidelines for assisting software system developers in setting quality goals, applying metrics and making quality assessments.

In spite of the significant body of research on traditional source code metrics, there has been a general failure to produce conclusive evidence as to their effectiveness for measuring software quality. We describe and recommend a potentially much more powerful and sensitive quality assessment alternative, software quality guidelines. Software quality guidelines are presented as “designer metrics”, that is, user-defined rules or constraints relating to measurable features of a program's structure, semantics, and syntax that affect its quality. To provide a methodology for designing, applying, and validating software quality guidelines, we recommend and briefly summarize IEEE standard 1061. This standard gives a process for constructing and implementing a software quality metrics framework that can be tailor-made to meet quality requirements for a particular project and/or organization. Our paper then demonstrates how software quality guidelines fit within the IEEE framework and gives an example illustrating how user-defined guidelines can be applied to evaluate or assess the quality of an Ada source unit. This guideline-based assessment of quality is then compared with an analysis based on traditional McCabe and Halstead metrics. Finally, we introduce a tool being developed by SAIC, called AdaReVu, as an effective mechanism for implementing and applying user-defined quality guidelines for Ada source code.

Empirical Analysis of Quality Models in Practice in Small IT Companies in SEE Region