A socio‐cognitive interpretation of the potential effects of downsizing on software quality performance

Organizational downsizing research indicates that downsizing does not always realize its strategic intent and may, in fact, have a detrimental impact on organizational performance. In this paper, we extend the notion that downsizing negatively impacts performance and argue that organizational downsizing can potentially be detrimental to software quality performance. Using social cognitive theory (SCT), we primarily interpret the negative impacts of downsizing on software quality performance by arguing that downsizing results in a realignment of social networks (environmental factors), thereby affecting the self‐efficacy and outcome expectations of a software professional (personal factors), which, in turn, affect software quality performance (outcome of behaviour undertaken). We synthesize relevant literature from the software quality, SCT and downsizing research streams and develop a conceptual model. Two major impacts of downsizing are hypothesized in the conceptual model. First, downsizing destroys formal and informal social networks in organizations, which, in turn, negatively impacts software developers' self‐efficacy and outcome expectations through their antecedents, with consequent negative impacts on software development process efficiency and software product quality, the two major components of software quality performance. Second, downsizing negatively affects antecedents of software development process efficiency, namely top management leadership, management infrastructure sophistication, process management efficacy and stakeholder participation with consequent negative impacts on software quality performance. This theoretically grounded discourse can help demonstrate how organizational downsizing can potentially impact software quality performance through key intervening constructs. We also discuss how downsizing and other intervening constructs can be managed to mitigate the negative impacts of downsizing on software quality performance.     

Software quality measurement

This article analyses our ability to measure software quality. The analysis is based on the representational theory of measurement. We analyse three assumptions on software quality measurement and recommend an increased measurement focus on establishing empirical relational systems (models of what we measure) in software quality related work. Applying measurement theory on the measurement of software quality related properties means that, we have a powerful tool to understand what we measure, what the meaningful operations on the measured values are, and how to interpret the results.     

Applications of information theory to software engineering measurement

In the 1990s, there is an emphasis on finding ways to lower software cost and improve quality. Thus, it is very important to quantify and measure factors, such as software complexity, which have been shown to affect cost and quality. Researchers have defined many software complexity measures, and have exploited them to identify fault-prone program modules, to predict the number of faults that testing and operation will reveal, or to assess maintainability. Information theory based software measures are attractive because they quantify, with a standard unit, the amount of information in an abstraction of a program. The unit of measure is a bit. The purpose of this paper is to survey the state of the art of applications of information theory to software measurement, beginning in 1972. Information theory based measures have been applied to most phases of the development lifecycle. However, there have been relatively few papers. Most measures have not been empirically validated. One can conclude that the field is in its infancy. Relevant concepts of information theory are briefly described, and tables summarize the references from various perspectives. Since the amount of research, thus far, has been very limited, researchers will find numerous opportunities to validate, refine, and improve the measures presented here. Such research should give future practitioners useful software measures for each phase of the lifecycle.     

The asymptotic model quality assessment for instrumental variable identification revisited

In this paper the problem of computing uncertainty regions for models identified through an instrumental variable technique is considered. Recently, it has been pointed out that, in certain operating conditions, the asymptotic theory of system identification (the most widely used method for model quality assessment) may deliver unreliable confidence regions. The aim of this paper is to show that, in an instrumental variable setting, the asymptotic theory exhibits a certain “robustness” that makes it reliable even with a moderate number of data samples. Reasons for this are highlighted in the paper through a theoretical analysis and simulation examples.     

基于灰色理论的软件测试质量评价方法

针对软件测试质量如何评价的问题,指出软件测试适合灰色理论应用,根据软件测试一般过程建立了软件测试质量模型,提出了基于灰色关联分析的软件测试质量评价方法.结果表明:该方法理论上先进,技术上可行,评价结果客观,为解决软件测试质量评价提供了一种新方法.     

软件质量要素的灰色关联分析及其应用

软件质量是软件开发相关各方共同关注的目标，定量评价软件质量是软件领域的难题。提出了运用灰色理论对软件质量模型的各要素进行灰色关联分析的技术方法和实例过程。结果表明，该方法理论上先进，技术上可行。     

Software quality by management learning from the manufacturing industries

This article looks at software quality from a managerial point of view. Despite the wealth of literature about software quality and how it is measured, high quality software systems are still alarmingly rare. It is suggested that DP management should look to modern production management for a lesson in how to promote quality in their products. This in turn means observing Japanese methods of management, in particular the innovation of the early eighties — the quality circle. Also investigated is the suspicion that today's DP staff would be unwilling to accept changes that management may be forced to adopt to improve the quality of software systems.     

Investigating organizational learning in eGovernment projects: A multi-theoretic approach

Organizational learning is essential during eGovernment implementation to leverage the benefits of such efforts. However, there is a lack of holistic understanding of organizational learning in IT-related change in public organizations. Motivated by such concerns, we construct a structurational framework that inter-relates elements pertinent to IT-related change from four theoretical perspectives: organizational politics, organizational culture, institutional theory, and organizational learning itself. Inter-relationships between elements of these perspectives are explicated through the notions of structures, modalities, and actions from structuration theory. Guided by the framework, we conducted a case study to examine the organizational learning occurring during a successful eGovernment project. The analysis indicates the inextricable interactions between elements from the different perspectives during the project implementation. The proposed framework offers both theoretical and practical benefits for understanding and facilitating organizational learning in eGovernment projects.     

基于模糊信息熵理论的教学软件质量综合评价模型

通过分析目前国内外使用的几种评估软件质量的方法,结合教学软件具体实际和综合考虑,提出利用模糊熵理论评估教学软件质量的模型,利用评价者评价完善权重知识库,该模型通过熵理论和评价指标体系,将主观权重和客观权重集结成综合权重,然后利用模糊理论进行处理得到最后处理结果,改善传统模糊理论评估方法主观因素影响过多的弊端,提高评价结果可信度,并能方便快捷地得出科学、准确、客观的评估,在实际应用中有良好的运行效果.     

The Automated Refinement of a Requirements Domain Theory

The specification and management of requirements is widely considered to be one of the most important yet most problematic activities in software engineering. In some applications, such as in safety critical areas or knowledge-based systems, the construction of a requirements domain theory is regarded as an important part of this activity. Building and maintaining such a domain theory, however, requires a large investment and a range of powerful validation and maintenance tools. The area of theory refinement is concerned with the use of training data to automatically change an existing theory so that it better fits the data. Theory refinement techniques, however, have not been extensively used in applications because of the problems in scaling up their underlying algorithms.In our work we have applied theory refinement to assist in the problem of validation and maintenance of a requirements theory concerning separation standards in the North East Atlantic. In this paper we describe an implemented refinement algorithm, which processes a logic program automatically generated from the theory. We overcame the size and expressiveness problems typically encountered when applying theory refinement to a logic program of this kind by designing focused, composite refinement operators within the algorithm. These operators modify the auto-generated logic program by generalising or specialising clauses containing ordinal relations—that is relations which operate on totally ordered data.     

模糊数学与中介真值理论相结合的评价方法

针对模糊非确定现象的评价问题,提出了模糊数学与中介真值理论相结合的评价方法。模糊数学评价法和中介真值理论的方法都是从量的角度研究和处理模糊现象。但是,模糊数学评价法注重应用而缺乏系统理论的支持,其模糊合成算子在多因素情况下很难确定,且度量值域局限于[0,1];中介真值理论的评价方法在处理因素较多且权重难以细分的情况时,也具有一定的局限性。因此,将模糊数学与中介真值相结合,将模糊数学的评价方法运用到二级指标的评定,将中介真值理论的评价方法运用到一级指标的综合评定,由此确定最佳选择方案。最后,将该方法运用于软件质量评估,并分别与模糊数学评价法和中介真值理论的评价方法相比较,结果表明该方法是可行的、合理的,并具有一定优势。     

Using Metaphor to Analyse Qualitative Data: Vulcans and Humans in Software Development

This paper reports on an experience of using metaphor in qualitative research of software engineering in practice. Our project aimed to uncover non-technical factors affecting the adoption and evolution of Software Quality Management Systems (referred to here as ‘the quality process’). Previously we have reported the tensions we uncovered around the quality process in four companies, based on semi-structured interviews. This paper extends this work by applying metaphor to the results. We show how we were able to produce more general statements regarding the tensions and their amelioration, and then introduce results from a fifth company, which we compare against our general statements. We find that these statements are generally supported by results from this fifth company. Finally we present some reflections on our experience of using metaphor in this way.     

基于灰色关联分析的软件用户界面评价

用户界面成为软件的重要组成部分,软件用户界面评价的作用日益重要。在分析常见软件质量模型的基础上,提出了软件用户界面质量模型,并应用灰色系统理论对用户界面质量要素进行关联分析,实现了用户界面的定量评价,同时,在计算灰色关联度时,根据质量要素重要性不同的事实引入了权重向量,以得到更为合理的评价结果。实例表明,该方法具有较好的实用性。     

Withholding effort in knowledge contribution: The role of social exchange and social cognitive on project teams

Few investigations have been made to determine what factors influence people in withholding knowledge from their colleagues. We created a construct, knowledge withholding (KW), defined as the likelihood that individuals contribute less knowledge to others in the organization than they could. We have formulated a model, based on social exchange theory and social cognition theory, to analyze the antecedents of KW from both personal and contextual perspectives. The contextual influencers were subdivided into dimensions of rational choice, normative conformity, and affective bonding to help in understanding KW. Results of a survey of 162 MIS alumni of a university, who had experienced software development, trust, distributive justice, and team-related work showed that personal outcome expectations had a substantial influence on KW.     

软件需求定量分析及其映射的模糊层次分析法

在用数量化理论3类(quantification theory of type 3,简称QT3)定量地分析软件需求的基础上,以质量功能展开(quality function deployment,简称QFD)中的质量屋(house of quality,简称HOQ)系列矩阵为纲领,基于由模糊技术改进后的模糊层次分析法(fuzzy analytic hierarchy process,简称FAHP),提出了一种软件需求定量分析及其向设计实现过程模糊映射的方法.将该方法具体应用于CD-R/RW光盘刻录机软件的开发过程,其有效性得到了验证.     

灰色推理技术及其智能应用研究

处理不确定性问题的能力已成为系统智能化水平高低的重要评价依据。本文提出了人工智能基于灰色信息的推理模型,该模型除进行符号模式匹配外,还进行灰色计算,是对模糊逻辑推理和Cantor集合理论的扩展;通过对软件质量因子的灰色关联分析,给出了软件质量评价的灰色计算模型;介绍了软件质量智能化灰色评价系（SQIGES）的基本结构及灰推理机的基本原理;得出了四个软件系统的质量评价结果。     

Developing a Quality of Experience (QoE) model for Web Applications

Web‐based service providers have long been required to deliver high quality services in accordance with standards and customer requirements. Increasingly, however, providers are required to think beyond service quality and develop a deeper understanding of their customers' Quality of Experience (QoE). Although models exist that assess the QoE of Web Application, significant challenges remain in: (1) Defining QoE factors from a Web engineering perspective, (2) quantifying the relationship between so‐called “objective” and “subjective” factors of relevance, and (3) dealing with limited data available in relation to subjective factors. In response, the work here presents a novel model (and associated software instantiation) that integrates factors through Key Performance Indicators and Key Quality Indicators. The mapping is incorporated into a correlation model that assesses the QoE of Web Applications, with a consideration of defining the factors in term of quality requirements derived from web architecture. The data resulting from the mapping is used as input of the proposed model to develop artefacts that quantify and predict QoE using Machine Learning. The development of proposed model is framed and guided by Design Science Research approach with the purpose of enabling providers to more informed decisions regarding QoE and/or to optimise resources accordingly. Although the work is oriented at developing an artefact that has clear utility for practice, the nascent design theory underpinning the work is developed and discussed.     

Intelligent agents and contracts: Is a conceptual rethink imperative?

The emergence of intelligent software agents that operate autonomously with little or no human intervention has generated many doctrinal questions at a conceptual level and has challenged the traditional rules of contract especially those relating to the intention as an essential requirement of any contract conclusion. In this paper, we will try to explore some of these challenges, and shed light on the conflict between the traditional contract theory and the transactional practice in the case of using intelligent software agents. We will try further to examine how intelligent software agents differ from other software applications, and consider then how such differences are legally relevant. This paper, however, is not intended to provide the final answer to all questions and challenges in this regard, but to identify the main components, and provide perspectives on how to deal with such issue.

Enterprise Data Quality: A Pragmatic Approach

Enterprise data—the data that is created, used and shared by a corporation in conducting business—is a critical business resource that must be analyzed, architected and managed with data quality as a guiding principle. This paper presents results, practical insights, and lessons learned from a large scale study conducted in the telecommunications industry that synthesizes data quality issues into an architectural and management approach. We describe the real life case study and show how requirements for data quality were collected, how the data quality metrics were defined, what guidelines were established for intersystem data flows, what COTS (commercial off-the-shelf) technologies were used, and what results were obtained through a prototype effort. As a result of experience gained and lessons learned, we propose a comprehensive data quality approach that combines data quality and data architectures into a single framework with a series of steps, procedures, checklists, and tools. Our approach takes into account the technology, process, and people issues and extends the extant literature on data quality.     

‘Best’ for whom?: the tension between ‘best practice’ ERP packages and diverse epistemic cultures in a university context

The idea that so-called ‘best’ business practices can be transferred to organizations when they purchase enterprise resource planning (ERP) software packages is a major selling point of these packages. Yet recent research has illustrated a gap between the espoused theory of a best practice solution and the theory-in-use experienced by those who install software with such a design. As researchers begin to examine the difficult process by which organizations recast the best practices model handed down to them by consultancies and software vendors in an effort to make the software ‘work for them’ in practice, it is equally important that we begin to understand the reasons that such a gap exists. To this end, we analyze the strategic partnership between a multinational software vendor and a university who together designed a ‘best practice’ ERP package for the higher education industry. Through the theoretical lens of ‘epistemic cultures’ we argue that in organizational contexts made up of more than one epistemic culture, the use of a best practice model will be problematic because, by definition, the model mandates one epistemological position through the software design. This is counter to a university's loosely coupled organizational form.