Software Quality Engineering Research Articles

Monitoring kth order runs in binary processes

Procedures for continuously monitoring binary attribute data processes are of utmost relevance for fields like electrical engineering, chemical production, software quality engineering, healthcare monitoring, and many more. In this article, new approaches are proposed, where kth order runs in a binary process are monitored. We derive methods for evaluating the performance of the new control charts, discuss computational issues of these methods and give design recommendations for the control charts. A real-data example demonstrates the successful application of the new control procedures.

AN EMPIRICAL STUDY OF FEATURE RANKING TECHNIQUES FOR SOFTWARE QUALITY PREDICTION

The primary goal of software quality engineering is to produce a high quality software product through the use of some specific techniques and processes. One strategy is applying data mining techniques to software metric and defect data collected during the software development process to identify potential low-quality program modules. In this paper, we investigate the use of feature selection in the context of software quality estimation (also referred to as software defect prediction), where a classification model is used to predict whether program modules (instances) are fault-prone or not-fault-prone. Seven filter-based feature ranking techniques are examined. Among them, six are commonly used, and the other one, named signal to noise ratio (SNR), is rarely employed. The objective of the paper is to compare these seven techniques for various software data sets and assess their effectiveness for software quality modeling. A case study is performed on 16 software data sets, and classification models are built with five different learners and evaluated with two performance metrics. Our experimental results are summarized based on statistical tests for significance. The main conclusion is that the SNR technique performs as well as the best performer of the six commonly used techniques.

The General Method Study and Practice for Trans-disciplinary Design in Internet Platform of 3D Art Museum

In the digital era of rapid development of computer software technology, the collaboration of science and arts becomes trend of development in terms of digital media content design and innovation. Breakthrough to the traditional working model, such as individual or independent small scale research and develop-ment team, establish a research and development team that consisting of artists and software engineers in art school, and do col-laborative research and development project is a necessary requirement for the progress of the digital age. This paper focuses on China Academy of Art Online Gallery project that integrates the transdisciplinary resources. By using art, technology, software engineering practice and quality management methods together, this project attempt to explore a set of basic methods of combining art and science to improve the standard of creation, research and design for China's Internet 3D technology platform. It is important to break with the traditional narrow way of thinking boldly and explore the general methods of multi-team collaboration for innovative projects. It has far-reaching strategic and practical significance for digital technological innovation research in art and design, online game and animation industry.

Behavioral economics in software quality engineering

This article describes empirical research results regarding the "history effect" in software quality evaluation processes. Most software quality models and evaluation processes models assume that software quality may be deterministically evaluated, especially when it is evaluated by experts. Consequently, software developers focus on the technical characteristics of the software product. A similar assumption is common in most engineering disciplines. However, in regard to other kinds of goods, direct violations of the assumption about objective evaluation were shown to be affected by the consequences of cognitive processes limitations. Ongoing discussion in the area of behavioral economics raises the question: are the experts prone to observation biases? If they are, then software quality models overlook an important aspect of software quality evaluation. This article proposes an experiment that aims to trace the influence of users' knowledge on software quality assessment. Measuring the influence of single variables for the software quality perception process is a complex task. There is no valid quality model for the precise measurement of product quality, and consequently software engineering does not have tools to freely manipulate the quality level for a product. This article proposes a simplified method to manipulate the observed quality level, thereby making it possible to conduct research. The proposed experiment has been conducted among professional software evaluators. The results show the significant negative influence (large effect size) of negative experience of users on final opinion about software quality regardless of its actual level.

Agile methods for software practice transformation

In a large-scale distributed complex software engineering environment, the ability to rapidly evaluate and improve software engineering practices can be a key market differentiator. Practices that shorten the development cycle, cost-effectively improve quality, and align software with customer needs can have a direct impact on the business value delivered by the enterprise. The IBM Quality Software Engineering (QSE) organization motivates and enables teams across IBM business units and geographies to adopt recommended software engineering practices. QSE has historically established communities and used well-known organizational change management principles to help teams adopt and use recommended practices. In 2008 and 2009, QSE discovered that blending well-known organizational change management principles with agile software development principles enables communities to more consistently deliver significant value to their members. QSE Communities use Scrum, which is an agile project management framework, to prioritize their work on the basis of the communities' needs, plan their work every two weeks and then deliver value at the end of every two-week Sprint (iteration), demonstrate results, and obtain feedback and continuously improve by reflecting and identifying improvement actions at the end of each Sprint. Combining Scrum with proven steps to organizational change management has allowed QSE to rapidly motivate and enable software engineering improvements across IBM.

REquirements, Aspects and Software Quality: the REASQ model.

Object-oriented analysis and design have been more concerned with system functionality, neglecting non-functional aspects; the result is code which is tangled and dicult to maintain, contradicting main principles of object orientation. Aspect Oriented Software Development (AOSD) proposes the early specification of non-functional requirements. However, a standard and homogenous vision of the AOSD terminology is still missing. The goal of this work is to integrate AOSD concepts, classic requirements engineering notions, and the new standard ISO/IEC 25030 on software quality requirements.The main result of this study is the REASQ (REquirements, Aspects and Software Quality) conceptual model, expressed in UML. All the modelling concepts are formalized into three related ontologies, representing the ambits of aspect-orientation, software quality and requirements engineering. The ontologies can be used as an umbrella to specify quality requirements in aspect-oriented engineering processes.

Patterns for Maude Metalanguage Applications

One of the most effective ways of improving the quality of software engineering, system design and development, and communication between the people concerned with these problems, is provided by software patterns. In this paper we present a set of basic patterns for Maude metalanguage applications. We show the viability of the defined patterns by comparing them to the developing approaches for several well-known Maude tools.

Investigating the effect of dataset size, metrics sets, and feature selection techniques on software fault prediction problem

Software quality engineering comprises of several quality assurance activities such as testing, formal verification, inspection, fault tolerance, and software fault prediction. Until now, many researchers developed and validated several fault prediction models by using machine learning and statistical techniques. There have been used different kinds of software metrics and diverse feature reduction techniques in order to improve the models’ performance. However, these studies did not investigate the effect of dataset size, metrics set, and feature selection techniques for software fault prediction. This study is focused on the high-performance fault predictors based on machine learning such as Random Forests and the algorithms based on a new computational intelligence approach called Artificial Immune Systems. We used public NASA datasets from the PROMISE repository to make our predictive models repeatable, refutable, and verifiable. The research questions were based on the effects of dataset size, metrics set, and feature selection techniques. In order to answer these questions, there were defined seven test groups. Additionally, nine classifiers were examined for each of the five public NASA datasets. According to this study, Random Forests provides the best prediction performance for large datasets and Naive Bayes is the best prediction algorithm for small datasets in terms of the Area Under Receiver Operating Characteristics Curve (AUC) evaluation parameter. The parallel implementation of Artificial Immune Recognition Systems (AIRS2Parallel) algorithm is the best Artificial Immune Systems paradigm-based algorithm when the method-level metrics are used.

Editorial: Testing practice and research

The first ‘Testing: Academic & Industrial Conference—Practice and Research Techniques’ (TAIC PART 2006) was held at Cumberland Lodge Windsor during 29–31 August 2006. The general chair was Mark Harman (King's College London), the programme chair was Phil McMinn (University of Sheffield), and the local arrangements chair was Zheng Li (King's College London). TAIC PART is firmly grounded in fostering collaboration between industry and academia. It aims to bring together industrial software developers and users together with academic researchers working on the theory and practice of software testing. TAIC PART 2006 was a unique, not-for-profit conference that combined what we believe to be the best aspects of three kinds of event: a formal academic conference, a research workshop, and a retreat. The aim was to act not only as a forum for the exchange of ideas, but also as a vehicle to stimulate, deepen, and widen partnership between the academia and industry in software testing internationally. In all, 54 delegates from 11 different countries, comprising 32 academics and 22 industrialists, attended the conference. The event featured two keynotes, regular paper sessions, a PhD symposium, and a ‘speed dating’ session devoted to stimulating collaboration between attendees. The first keynote was given by Bill Woodworth, Corporate Director of IBM Quality Software Engineering. Bill spoke on test management at IBM, of which he has over 25 years of experience. John Hatcliff, who delivered the second keynote, is Professor in the Computing and Information Sciences Department at Kansas State University. John spoke on his internationally leading work on testing and software model checking. TAIC PART 2006 received a total of 50 full-paper submissions. After a rigorous reviewing process, 24 papers were accepted, with 8 of those papers from industry, 10 papers containing academic research, and a further 6 short papers accepted for the special PhD programme. Accepted papers covered a wide spectrum of state-of-the-art testing practice and research, including fault prediction, model-based testing, test specifications, the testing life cycle, search-based testing, database testing, web service testing, test requirements analysis, integration testing, empirical and case studies, and industrial challenges. The conference proceedings were published by IEEE and are available online. Two papers in this special issue are extended versions of some of the best papers originally presented at the conference. They have also been through a further reviewing process. The first paper is the result of an academic–industrial collaboration. In their paper ‘Quality Assurance for TTCN-3 Test Specifications’, Helmut Neukirchen, Benjamin Zeiss, and Jens Grabowski, of the University of Göttingen, and Paul Baker and Dominic Evans, of Motorola Labs, propose a technique to (1) assess the quality of existing test suites through metrics; (2) improve it through refactoring; and (3) detect refactoring opportunities by means of rules that are based on quality metrics. They focus on test suites expressed in the Testing and Test Control Notation TTCN-3, a language designed to support the specification of test suites in the telecommunication domain. The quality attribute considered in this work is maintainability, decomposed into analysability and changeability. Size, complexity, and coupling metrics are defined to characterize such quality attributes. The refactoring catalogue includes 23 TTCN-3-specific refactorings and 28 Java refactorings that are applicable to TTCN-3 as well. Eight rules are defined to check the applicability of refactorings automatically. These are implemented in a tool called ‘TRex’. The second paper, entitled ‘Automated Discovery of State Transitions and their Functions in Source Code’, by Neil Walkinshaw, Shaukat Ali, Kirill Bogdanov, and Mike Holcombe, presents a technique to reverse engineer source code into a state machine. It allows a developer to identify the states at a given point and statements that are responsible for state transitions. The technique also combines several ingredients, including symbolic execution and state abstraction, and is demonstrated with examples. Finally, we are grateful to our sponsors, whose financial contributions made it possible for TAIC PART 2006 to happen. Funding was received from the EPSRC and also from industry, including support from DaimlerChrysler, Ericsson, IPL Ltd., LDRA Ltd., Motorola, and Vizuri. The TAIC PART website (http://www2006.taicpart.org) serves as lasting resource to the event, containing the programme, photographs, and downloadable presentations of all the talks.

Length and readability of structured software engineering abstracts

Attempts to perform systematic literature reviews have identified a problem with the quality of software engineering abstracts for papers describing empirical studies. Structured abstracts have been found useful for improving the quality of abstracts in many other disciplines. However, there have been no studies of the value of structured abstracts in software engineering. Therefore this paper aims to assess the comparative length and readability of unstructured abstracts and structured versions of the same abstract. Abstracts were obtained from all empirical conference papers from the Evaluation and Assessment in Software Engineering Conference (EASE04 and EASE06) that did not have a structured abstract (23 in total). Two novice researchers created structured versions of the abstracts, which were checked by the papers' authors (or a surrogate). Web tools were used to extract the length in words and readability in terms of the Flesch reading ease index and automated readability index (ARI) for the structured and unstructured abstracts. The structured abstracts were on average 142.5 words longer than the unstructured abstracts (p<0.001). The readability of the structured abstracts was better by 8.5 points on the Flesch index (p<0.001) and 1.8 points on the ARI (p<0.003). The results are consistent with previous studies, although the increase in length and the increase in readability are both greater than the previous studies. Future work will consider whether structured abstracts increase the content and quality of abstracts.

In search for a widely applicable and accepted software quality model for software quality engineering

Software Quality Engineering is an emerging discipline that is concerned with improving the approach to software quality. It is important that this discipline be firmly rooted in a quality model satisfying its needs. In order to define the needs of this discipline, the meaning of quality is broadly defined by reviewing the literature on the subject. Software Quality Engineering needs a quality model that is usable throughout the software lifecycle and that it embraces all the perspectives of quality. The goal of this paper is to propose the characteristics of a quality model suitable for such a purpose, through the comparative evaluation of existing quality models and their respective support for Software Quality Engineering.

Measuring size, complexity, and coupling of hypergraph abstractions of software: An information-theory approach

Software development is fundamentally based on cognitive processes. Our motivating hypothesis is that amounts of various kinds of information in software artifacts may have useful statistical relationships with software-engineering attributes. This paper proposes measures of size, complexity and coupling in terms of the amount of information, building on formal definitions of these software-metric families proposed by Briand, Morasca, and Basili. Ordinary graphs represent relationships between pairs of nodes. We extend prior work with ordinary graphs to hypergraphs representing relationships among sets of nodes. Some software engineering abstractions, such as set-use relations for public variables, are better represented as hypergraphs than ordinary (binary) graphs. Traditional software metrics are based on counting. In contrast, we adopt information theory as the basis for measurement, because the design decisions embodied by software are information. This paper proposes software metrics of size, complexity, and coupling based on information in the pattern of incident hyperedges. For comparison, we also define corresponding counting-based metrics. Three exploratory case studies illustrate some of the distinctive features of the proposed metrics. The case studies found that information theory-based software metrics make distinctions that counting metrics do not, which may be relevant to software engineering quality and process. We also identify situations when information theory-based metrics are simply proportional to corresponding counting metrics.

EPP for Improving Project Quality in Software Engineering Based E-learning Development Process

EPP for Improving Project Quality in Software Engineering Based E-learning Development Process

Software Quality Engineering: Testing, Quality Assurance, and Quantifiable Improvement. By Jeff Tian. Published jointly by John Wiley & Sons, Inc., Hoboken, NJ, U.S.A. and IEEE Computer Society Press, Los Alamitos, CA, U.S.A., 2005. ISBN: 0‐471‐71345‐7, pp 412

Software Quality Engineering: Testing, Quality Assurance, and Quantifiable Improvement. By Jeff Tian. Published jointly by John Wiley & Sons, Inc., Hoboken, NJ, U.S.A. and IEEE Computer Society Press, Los Alamitos, CA, U.S.A., 2005. ISBN: 0‐471‐71345‐7, pp 412

A new approach in development of distributed framework for automated software testing using agents

Software testing is the technical kernel of software quality engineering, and to develop critical and complex software systems not only requires a complete, consistent and unambiguous design, and implementation methods, but also a suitable testing environment that meets certain requirements, particularly, to face the complexity issues. Traditional methods, such as analyzing each requirement and developing test cases to verify correct implementation, are not effective in understanding the software’s overall complex behavior. In that respect, existing approaches to software testing are viewed as time-consuming and insufficient for the dynamism of the modern business environment. This dynamics requires new tools and techniques, which can be employed in tandem with innovative approaches to using and combining existing software engineering methods. This work advocates the use of a recently proposed software engineering paradigm, which is particularly suited to the construction of complex and distributed software-testing systems, which is known as Agent-Oriented Software Engineering. This methodology is a new one, which gives the basic approach to agent-based frameworks for testing.

Software Project Management and Quality Engineering Practices for Complex, Coupled Multiphysics, Massively Parallel Computational Simulations: Lessons Learned From ASCI

Many institutions are now developing large-scale, complex, coupled multiphysics computational simulations for massively parallel platforms for the simulation of the performance of nuclear weapons and certification of the stockpile, and for research in climate and weather prediction, magnetic and inertial fusion energy, environmental systems, astrophysics, aerodynamic design, combustion, biological and biochemical systems, and other areas. The successful development of these simulations is aided by attention to sound software project management and software engineering. We have developed “lessons learned” from a set of code projects that the Department of Energy National Nuclear Security Agency has sponsored to develop nuclear weapons simulations over the last 50 years. We find that some, but not all, of the software project management and development practices (rather than processes) commonly employed for non-technical software add value to the development of scientific software and we identify those that we judge add value. Another key finding, consistent with general software industry experience, is that the optimal project schedule and resource level are solely determined by the requirements once the requirements are fixed.

Requirements Engineering and Process Modelling in Software Quality Management— Towards a Generic Process Metamodel

This paper examines the concept of Quality in Software Engineering, its different contexts and its different meanings to various people. It begins with a commentary on quality issues for systems de...

Structural analysis of the impact of knowledge creation and knowledge embedding on software process capability

Improving the capability of the development process has emerged as an important strategy for addressing recurring problems in software development, such as poor quality, high development costs, and long delivery lead times. While a number of normative software process models have been proposed, limited theory development has occurred. This study adopts a knowledge management perspective to examine the antecedents of software process capability. Synthesizing prior research in total quality management (TQM), software engineering and knowledge management, it is posited that creation of process knowledge and embedding the knowledge in the process are important antecedents of software process capability. Two models that relate these antecedents to software process capability are tested using data collected from information systems (IS) units in Fortune 1000 firms. The first model is a direct-impact model, which posits that both knowledge creation and knowledge embedding have a direct and positive impact on software process capability. The second model is a mediated-impact model, which posits that knowledge embedding completely mediates the relationships between knowledge creation and software process capability. The results provide strong support to the mediated-impact model suggesting that the extent to which knowledge is embedded in the process has a significant positive effect on software process capability. The results also suggest that the stock of knowledge embedded in the process is influenced by the extent to which knowledge is created in the context of the development process. Implications of these findings for IS research, theory, and practice are discussed.

Land and water resources model integration: software engineering and beyond

Integrated modelling has arisen as a valuable tool for assisting environmental management. Many existing land and water resources models are, however, not amenable to integration. To transform and develop models that serve integrated environmental management we need to address technical and social issues that include model reformulation and design, adoption of standard specification, notation and documentation on models and the reluctance of developers to adopt new design and programming approaches. There are tools and approaches that can aid the adoption of new approaches, and these are reviewed in the paper. Other developments that need to be infiltrated into the modelling community include facilities to flexibly examine, edit, visualise and analyse data at all stages of program execution, processes for user involvement in program and interface design, tools to assist knowledge-based selection of models and processes for undertaking and recording the peer reviewing of models. Options to address these issues are discussed, and it is considered that, despite the availability of suitable tools, the likelihood of getting adoption of high quality software engineering along with appropriate documentation and design practices will be low unless an integrated development approach is made, and the benefits made apparent to both developers and users. Context abstract: recent moves towards more integrated management of land and water resources have resulted in an increased focus on model integration. This focus has included the redesign of existing models to increase their capacity for reuse, often by using object-oriented approaches to produce flexible and reusable program modules. The redesign of existing models, as well as the design of new models, has, ideally, being done in a way that allows the program modules to be easily linked and run together. The adoption of sound software engineering practices, including the use of formalised modelling languages and modular development, is helping to achieve the goal of producing natural resources management modelling tool kits that allow complex, module-based, programs to be developed through drag-and-drop environments. Beyond software engineering there lie a number of issues that must be considered before the advantages of drag-and-drop integrated environmental modelling become apparent to natural resources managers and planners. These issues, including model design, notation, documentation and review, are discussed.

Improving quality in software engineering through emphasis on communication

Improving quality in software engineering through emphasis on communication