A novel approach to software quality risk management

Software quality is very important in today's competitive business environment. It is a critical constraint on software projects. Software organizations’ major objectives are delivering products on time and achieving quality goals. Quality is directly dependent on software processes, which are inherently variable and uncertain, involving substantial risk. Managing quality risk is an important challenge. The conventional approach to quality risk management for ongoing software processes has two major deficiencies: static analytic models are used, and structured methodologies to enhance processes and improve quality are not systematically applied. This new practical method uses Six Sigma and Monte Carlo Simulation for ongoing quality risk management. DMAIC (Define, Measure, Analyse, Improve, Control) is systematically applied as a tactical framework to enhance the process and improve quality. Simulation predicts quality (reliability) at the expected process end and identifies and quantifies risk. DMAIC is a verified structured methodology for systematic process and quality improvements. Monte Carlo Simulation is superior to conventional risk models. These synergetic enhancements eliminate observed deficiencies. The method has been successfully proven and applied practically on real in‐house projects. Substantial savings, quality and customer satisfaction have been achieved. An application on an internal project and obtained results are presented. The method is simplistically elaborated on a published third‐party project answering key research questions from practical perspectives. This CMMI® compliant method offers important benefits including savings, quality and customer satisfaction. Copyright © 2013 John Wiley & Sons, Ltd.     

A knowledge-based evolutionary assistant to software development project scheduling

The scheduling of software development projects is a central, non-trivial and costly task for software companies. This task is not exempt of erroneous decisions caused by human limitations inherent to project managers. In this paper, we propose a knowledge-based evolutionary approach with the aim of assisting to project managers at the early stage of scheduling software projects. Given a software project to be scheduled, the approach automatically designs feasible schedules for the project, and evaluates each designed schedule according to an optimization objective that is priority for managers at the mentioned stage. Our objective is to assign the most effective set of employees to each project activity. For this reason, the evaluation of designed schedules in our approach is developed based on available knowledge about the competence of the employees involved in each schedule. This knowledge arises from historical information about the participation of the employees in already executed projects. In order to evaluate the performance of our evolutionary approach, we present computational experiments developed over eight different sets of problem instances. The obtained results are promising since this approach has reached an optimal level of effectivity on seven of the eight mentioned sets, and a high level of effectivity on the remaining set.     

Six sigma as a business process management method in services: analysis of the key application problems

Apart from being applied in production, Six Sigma has grown considerably in importance as a business process management (BPM) method in services. The transfer of the method from production to service applications does, however, pose problems which affect the success of implementing Six Sigma as a BPM method in services. For a successful application of the Six Sigma method in services it is helpful to know these possible problems in order to be able to avoid them and to achieve the goals of Six Sigma improvement projects. In addition from a theoretical view knowing the problems is a first step for further development of the Six Sigma method. Therefore, the present article deals with the following questions: which key problems regarding the application of Six Sigma in services are mentioned in the literature and which phases of the Six Sigma cycle can they be assigned to? The existing literature on this subject is reviewed and a qualitative content analysis of its contents is presented. Additionally, a survey based on the problems found in the literature is conducted to compare theory and practice. The evaluation of the results shows, on the one hand, that to date the problems of the application of Six Sigma in services have little been dealt with. On the other hand the evaluation provides a survey of the problems structured in accordance to the phases of the Six Sigma cycle. Thus, the present paper offers new findings as regards the state-of-the-art of the Six Sigma method.     

Design for Six Sigma through collaborative multiobjective optimization

This paper presents a global methodology for designing product for Six Sigma. First, we combine a feasibility-modeling technique with an interactive multiobjective algorithm taking into account the decision maker’s preferences (IMOP) to generate several Pareto-optimal solutions that maintain a probability of constraint satisfaction. These solutions are called reliable Pareto-optimal solutions.The solutions found by the algorithm fulfill as much as possible the decision makers’ requirements. Second, we develop a procedure for choosing a solution for implementation from among the reliable Pareto-optimal solutions generated by the algorithm. This procedure is based on the robust design and philosophy of Six Sigma. Finally, the critical characteristics are identified to help the managers develop the manufacturing system and its related control plans in order to achieve quality products. The proposed methodology is applied to vehicle crash-worthiness design optimization for side impact with structural weight and front door velocity under side impact as objectives.     

六西格玛方法在过程改造中的应用简介

介绍了六西格玛(6σ)的概念、基本评价指标及其开发的DMAIC流程的含义与主要分析工具,并介绍了六西格玛法在过程优化中的应用。该过程是一生产己内酰胺的连续过程,6σ利用统计学工具如量规分析、方差分析对过程的各个生产单元进行分析,找出各个生产单元的关键输入变量和输出变量及其关系,确定影响产品产量的瓶颈,包括操作条件和低效或陈旧的仪表与设备,并用实验设计验证以上分析结果,最后消除瓶颈,达到提高产量的目的。     

A PRACTICAL APPROACH TO PRIORITIZE PROJECT ACTIVITIES THROUGH FUZZY RANKING

Effective management of project activities is a critical issue for project managers, especially when the projects are complex. This is more essential for projects where the number of activity attributes is numerous. In such circumstances, prioritizing of project activities is an important issue and can enable better management of projects by focusing more on the most critical activities. However, in many cases prioritizing project activities can be a difficult task because some of the activity attributes might not be expressed easily in crisp terms. In this article, a practical fuzzy rating and ranking approach is proposed to prioritize project activities with fuzzy attributes. The proposed approach is also tested through an example problem from the literature.     

A method to select a successful interoperability solution through a simulation approach

Enterprise applications and software systems need to be interoperable in order to achieve seamless business across organizational boundaries and thus realize virtual networked organizations. Our proposition can be considered as an interoperability project selection approach and is based on three steps: (1) Modelling both collaborative business processes and potential related interoperability projects; (2) Evaluating the accessibility of each project regarding the current state of the organization; (3) Simulating each project and assessing the associated performance. These results are finally projected on a comparison matrix used as a decision support to select the most appropriate interoperability solution. An application case extracted from the French aerospace sector demonstrates the applicability and the benefits of the proposition.     

Evaluation of advanced industrial control projects: a framework for determining economic benefits

A framework for the evaluation of advanced control projects is illustrated in this paper by way of a case study on level control in mineral flotation. The approach taken is to investigate how improvements brought about by advanced control can be measured to a required level of statistical significance, after the controller has been commissioned. Measured improvements are translated into increases in cash flow that result from implementing an advanced controller, which is then used for project evaluation. As an introduction to the case study, a framework for establishing advanced controllers for industrial processes, which culminates in determining economic benefits, is discussed. Ways of obtaining controller benefits through the reduction of downtime and product variations, are also described. The risk of implementing an advanced control project is discussed together with typical scenarios under which such projects are often undertaken.     

Comparing senior executive and project manager perceptions of IT project risk: a Chinese Delphi study

The success rate for information technology (IT) projects continues to be low. With an increasing number of IT projects in developing countries such as China, it is important to understand the risks they are experiencing on IT projects. To date, there has been little research documenting Asian perceptions of IT project risk. In this research, we examine the risks identified by Chinese senior executives (SEs) and project managers (PMs), and compare these two groups. The importance of top management support in IT projects is well documented. Prior research has shown that from the perspective of IT PMs, lack of support from SEs is the number one risk in IT projects. Surprisingly, senior executives' perceptions towards IT project risk have never been systematically examined. One reason why lack of support from senior executives continues to represent a major risk may be that senior executives themselves do not realize the critical role that they can play in helping to deliver successful projects. In this study, we use the Delphi method to compare the risk perceptions of senior executives and project managers. By comparing risk factors selected by each group, zones of concordance and discordance are identified. In terms of perceived importance ascribed to risk factors, PMs tend to focus on lower‐level risks with particular emphasis on risks associated with requirements and user involvement, whereas SEs tend to focus on higher‐level risks such as those risks involving politics, organization structure, process, and culture. Finally, approaches for dealing with risk factors that are seen as important by both SEs and PMs are provided.     

Harmonization simulation model for managing several stochastic projects

A highly complicated project management system including several simultaneously realized PERT-COST type network projects, is considered. The projects are of different importance and significance; for each project its corresponding priority index is pre-given. The total budget at the project management disposal to carry out all the projects, is limited. Given for each project its priority value, the problem is to determine optimal budget assignments and optimal due dates of accomplishing each project, to maximize the weighted sum of the projects’ utilities. The problem centers on maximizing the system’s utility by implementing the harmonization model for each single project, which has been outlined in our previous publication [Math. Comput. Simul. (2002)]. Thus, the paper under consideration is an essential extension of that publication.The system’s harmonization model comprises two levels. At the upper level a high-speed lookover search algorithm is implemented, together with a partial harmonization model to determine the projects’ reliability values. At the lower level a linear programming model is imbedded under certain assumptions.     

Managing uncertainty in software projects

Although it is impossible to predict problems that will occur in software projects, project managers can employ strategies that imbue their projects with greater resilience. Throughout a software project, a series of practices can be established to manage uncertainties. This paper proposes an approach to managing uncertainty in software projects. The approach seems to improve project performance and success. This work is based on the principles of evidence-based software engineering. We conduct an exploratory literature search and a systematic literature review. In addition, we carry out action research in a software development project. Semi-structured interviews were conducted to evaluate and improve this approach. Finally, we held a focus group to evaluate the final proposed approach. The exploratory review helped to characterise the difference between risk and uncertainty. The systematic literature review revealed five methods and 18 practices for reducing uncertainties. The action research applied some of these techniques and investigated whether they contributed to a better uncertainty management. In the semi-structured interviews, practical points of view were added to the approach. This work defines an approach to uncertainty management and describes strategies that allow team members to explicitly formalise and manage uncertainty in software projects.     

Blending CMM and Six Sigma to meet business goals

Tata Consultancy Services (TCS) blended Six Sigma concepts with the various SW CMM key process areas, thereby creating a quality management system. This helped TCS improve its customer focus and sustain process improvement initiatives by explicitly linking them to business goals. The TCS team implemented the QMS on the lines of Level 2 and 3 requirements of SW CMM, using Six Sigma concepts to reinforce quantitative process and product measurements and analysis, process improvements for defect prevention, and process optimization. This article describes TCS's approach, highlighting the benefits gained by blending Six Sigma and CMM to provide quality deliverables to its customers.     

A multi-objective particle swarm optimization for project selection problem

Selecting the most appropriate projects out of a given set of investment proposals is recognized as a critical issue for which the decision maker takes several aspects into consideration. Since many of these aspects may be conflicting, the problem is rendered as a multi-objective one. Consequently, we consider a multi-objective project selection problem in this study where total benefits are to be maximized while total risk and total coat must be minimized, simultaneously. Since solving an NP-hard problem becomes demanding as the number of projects grows, a multi-objective particle swarm with new selection regimes for global best and personal best for swarm members is designed to find the locally Pareto-optimal frontier and is compared with a salient multi-objective genetic algorithm, i.e. SPEAII, based on some comparison metrics with random instances.     

A multi-method, holistic strategy for researching critical success factors in IT projects

In the field of information systems (IS) there is an observable trend towards the use of multi-method research. Using different research methods allows for the cross-validation of data obtained via multiple approaches, with the potential to increase the robustness of research results. Such a multi-method approach is applicable to a comprehensive research agenda on critical success factors, an agenda that needs to take into account not only the identification, but also the analysis and management of critical success factors. The goal of this article is to contribute new knowledge on how to carry out research on critical success factors in IS projects using a multi-method approach. For this purpose, two research projects are presented, each a variation of the research design customized to particular circumstances. First, there is an outline of the research approach taken for a critical success factor research project in the field of portal implementation, with discussion of the strengths and weaknesses of the project. Taking into consideration these experiences, the research approach of a similar critical success factor research project in the field of offshore software development is then described. Finally, recommendations for using the multi-method research approach in critical success factor research are presented.     

Continuous improvement of knowledge management systems using Six Sigma methodology

Knowledge retrieval is a decisive part of the performance of a knowledge management system. In order to enhance retrieval accuracy, an effective performance evaluation mechanism is necessary. Nowadays, there is not a standard evaluation framework for knowledge retrieval evaluation, because the evaluation set up is still technology-dependent, focusing on specific elements of the search context. The laboratory-based evaluation is not suitable to evaluate the knowledge retrieval process, since knowledge is dynamic, constantly changing and evolving. Besides, ambiguous query is also an important factor for the performance of knowledge retrieval systems. In order to improve the performance of knowledge retrieval, this paper proposes an evaluation mechanism using Six Sigma methodology to help developers continuously control the knowledge retrieval process. Specifically, this study involves the following tasks: (i) proposes a general knowledge retrieval framework based on the analysis result of knowledge retrieval, (ii) designs the knowledge retrieval evaluation framework using Six Sigma's Define-Measure-Analyze-Improve-Control (DMAIC) process and (iii) develops the related technologies to implement the knowledge retrieval evaluation mechanism. The knowledge retrieval evaluation mechanism allows system developers to maintain the knowledge retrieval system with ease and meanwhile enhance the accuracy.     

A fuzzy real option approach for investment project valuation

The main purpose of this paper is to propose a fuzzy approach for investment project valuation in uncertain environments from the aspect of real options. The traditional approaches to project valuation are based on discounted cash flows (DCF) analysis which provides measures like net present value (NPV) and internal rate of return (IRR). However, DCF-based approaches exhibit two major pitfalls. One is that DCF parameters such as cash flows cannot be estimated precisely in the uncertain decision making environments. The other one is that the values of managerial flexibilities in investment projects cannot be exactly revealed through DCF analysis. Both of them would entail improper results on strategic investment projects valuation. Therefore, this paper proposes a fuzzy binomial approach that can be used in project valuation under uncertainty. The proposed approach also reveals the value of flexibilities embedded in the project. Furthermore, this paper provides a method to compute the mean value of a project’s fuzzy expanded NPV that represents the entire value of project. Finally, we use the approach to practically evaluate a project.     

A model of information systems development project performance

abstract. Performance in information systems development (ISD) projects can be critical to business success. But, while project performance has been the subject of much debate, there has been little empirical research into its determinants. A survey of IS projects in the UK and New Zealand is analysed to test hypotheses concerning performance in terms of both project completion and budget (time-cost) variances. Subsequently, a secondary analysis of the findings is used to build a more complete empirical model of project performance. The paper helps to develop the theory of IS development project performance and also has significant implications for practice. Discussion of the findings highlights the importance of project team dynamics, risk management, senior management support for strategic projects and user participation in ensuring successful IS project performance.     

Protecting research and technology from espionage

In recent years, governmental and industrial espionage becomes an increased problem for governments and corporations. Especially information about current technology development and research activities are interesting targets for espionage. Thus, we introduce a new and automated methodology that investigates the information leakage risk of projects in research and technology (R&T) processed by an organization concerning governmental or industrial espionage. Latent semantic indexing is applied together with machine based learning and prediction modeling. This identifies semantic textual patterns representing technologies and their corresponding application fields that are of high relevance for the organization’s strategy. These patterns are used to estimate organization’s costs of an information leakage for each project. Further, a web mining approach is processed to identify worldwide knowledge distribution within the relevant technologies and corresponding application fields. This information is used to estimate the probability that an information leakage occur. A risk assessment methodology calculates the information leakage risk for each project. In a case study, the information leakage risk of defense based R&T projects is investigated. This is because defense based R&T is of particularly interest by espionage agents. Overall, it can be shown that the proposed methodology is successful in calculation the espionage information leakage risk of projects. This supports an organization by processing espionage risk management.     

A staff to job assignment (partitioning) problem with multiple objectives

A 0–1 goal programming (GP) model is presented, based on an actual case example, for assigning projects to engineers in order to prevent project splitting and excessive manpower requirements, complete as many preferred projects as possible and maximize profits while keeping a balanced workload. For assigning 15 projects to 6 engineers, the GP model contains 90 0–1 decision variables and 28 goals grouped into 5 priorities. This problem could not be solved with 0–1 GP codes that were readily available. It was, however, easily solved under different priority structures, in a few CPU seconds, using a heuristic 0–1 GP method. The approach is simple and naturally understood by management, and it can easily be adopted to other similar situations.     

HSPM: A Better Model to Effectively Preventing Open-Source Projects from Dying

With the rapid development of Open-Source (OS), more and more software projects are maintained and developed in the form of OS. These Open-Source projects depend on and influence each other, gradually forming a huge OS project network, namely an Open-Source Software ECOsystem (OSSECO). Unfortunately, not all OS projects in the open-source ecosystem can be healthy and stable in the long term, and more projects will go from active to inactive and gradually die. In a tightly connected ecosystem, the death of one project can potentially cause the collapse of the entire ecosystem network. How can we effectively prevent such situations from happening? In this paper, we first identify the basic project characteristics that affect the survival of OS projects at both project and ecosystem levels through the proportional hazards model. Then, we utilize graph convolutional networks based on the ecosystem network to extract the ecosystem environment characteristics of OS projects. Finally, we fuse basic project characteristics and environmental project characteristics and construct a Hybrid Structured Prediction Model (HSPM) to predict the OS project survival state. The experimental results show that HSPM significantly improved compared to the traditional prediction model. Our work can substantially assist OS project managers in maintaining their projects’ health. It can also provide an essential reference for developers when choosing the right open-source project for their production activities.