Identifying Suitable Representation Techniques for the Prioritization of Requirements and Their Interdependencies for Multiple Software Product Lines

Software requirements typically do not exist independently of each other, rather most requirements have some type of dependency on another requirement [4]. For companies developing software products, which depend on each other, in so-called multiple software product lines (SPLs), systematic requirements management, including consideration for prioritization and inter‐ dependencies, is a time-consuming and convoluted task. Representation techniques for complex requirements can convey critical requirements interdependency information to make prioritization of requirements quicker and more accurate [1]. Based on reviewing the foremost literature, this paper identifies the representation techniques for requirements management which are most suitable for multiple software product lines (SPLs

Understanding and supporting large-scale requirements management

Large market-driven software companies face new challenges in requirements engineering and management that emerged due to their recent extensive growth. At the same time, the pressure generated by competitors’ and users’ expectations demands being more competitive, creative and flexible to more quickly respond to a rapidly changing market situation. In the pursuit of staying competitive in this context, new ideas on how to improve the current software engineering practice are requested to help maintaining the engineering efficiency while coping with growing size and complexity of requirements engineering processes and their products. This thesis focuses on understanding and supporting large-scale requirements management for developing software products to open markets. In particular, this thesis focuses on the following requirements management activities in the mentioned context, namely: scope management, variability management and requirements consolidation. The goals of the research effort in this thesis are to provide effective methods in supporting mentioned requirements management activities in a situation when the size of them and their complexity require large time and skills efforts. Based on empirical research, where both quantitative and qualitative approaches were utilized, this thesis reports on possible improvements for managing variability and presents visualization techniques to assist scope management for large-scale software product development contexts. Both reported ideas are empirically evaluated in case studies in a large-scale context. Additionally, the benefits of using linguistic methods for requirements consolidation are investigated in a replicated experimental study based on a relevant industry scenario

Impact estimation: IT priority decisions

Given resource constraints, prioritization is a fundamental process within systems engineering to decide what to implement. However, there is little guidance about this process and existing IT prioritization methods have several problems, including failing to adequately cater for stakeholder value. In response to these issues, this research proposes an extension to an existing prioritization method, Impact Estimation (IE) to create Value Impact Estimation (VIE). VIE extends IE to cater for multiple stakeholder viewpoints and to move towards better capture of explicit stakeholder value. The use of metrics offers VIE the means of expressing stakeholder value that relates directly to real world data and so is informative to stakeholders and decision makers. Having been derived from prioritization factors found in the literature, stakeholder value has been developed into a multi-dimensional, composite concept, associated with other fundamental system concepts: objectives, requirements, designs, increment plans, increment deliverables and system contexts. VIE supports the prioritization process by showing where the stakeholder value resides for the proposed system changes. The prioritization method was proven to work by exposing it to three live projects, which served as case studies to this research. The use of the extended prioritization method was seen as very beneficial. Based on the three case studies, it is possible to say that the method produces two major benefits: the calculation of the stakeholder value to cost ratios (a form of ROI) and the system understanding gained through creating the VIE table

Information Technology Project Prioritization

This thesis provides a contemporary review of several topics related to information technology project prioritization, which will help managers create their own custom methodology. Traditional prioritization tools such as weighted average scoring models are used for simultaneous comparison of a number of proposed projects on multiple dimensions, to facilitate alignment with organization goals. These methods are used for the analysis of information related to the weight preferences over criteria used. If used correctly with this procedure, it is possible to bring forward an authentic figure of merit, which is used as the projects strategic potential. This allows the projects to be ranked and the highest-ranking projects to be considered for selection. Visual tools can then be used for selection of optimum project portfolio. The literature dedicates less time on tools beyond the selection of projects. This study aims to bridge this gap by proposing a final phase of project prioritization as Project Portfolio Management

Customized risk assessment in military shipbuilding

This paper describes a customized risk assessment framework to be applied in military shipbuilding projects. The framework incorporates the Delphi method with visual diagrams, Bayesian Networks (BN) and the expression of expert opinions through linguistic variables. Noisy-OR and Leak Canonical models are used to determine the conditional probabilities of the BN model. The approach can easily be adapted for other shipbuilding construction projects. The visual diagrams that support the Delphi questionnaire favor the comprehensive visualization of the interdependencies between risks, causes, risks and causes, and risks and effects. The applicability of the framework is illustrated through the assessment of risk of two real military shipbuilding projects. This assessment includes a sensitivity analysis that is useful to prioritize mitigation actions. In the two cases studies, the risks with higher probability of occurrence were failures or errors in production, of the contracted, in the requirements, and in planning. The results of the sensitivity analysis showed that a set of mitigation actions directed at relatively easily controllable causes would have achieved important reductions in risk probabilities.- (undefined

Advancing the maturity of project portfolio management through methodology and metrics refinements

This thesis presents enhancements to the theory of and practices in project portfolio management, specifically in refinements to methodology, measurement and alignment with strategic planning. Project portfolio management is the practice for evaluating, selecting and managing in an integrated manner a portfolio, which consist of projects, programs and other related work. Several studies on current practices in portfolio management have indicated a rather low maturity level of organizations in regard to project portfolio management. From the perspective of theory, this appears to be both the result of a relatively immature discipline and a rather technical approach to what is a human decision problem. Whereas salient literature focuses on portfolio management as a constrained optimization problem, it is suggested that it is necessary to define the complexity challenge of portfolio management beyond the mathematical aspect. In this respect, the managerial aspect of dealing with uncertainty and dynamic goals and constraints, the process aspect of an iterative and complex business problem and the behavioral aspect characterized by cognitive limitations, bounded rationality and political bias need to be captured. This thesis addresses several of these complexity aspects, which are based on knowledge from the project management discipline, as well as other scientific disciplines, specifically decision, behavioral and management science. Contributions to theory and practice of project portfolio management focus on several areas. The author lays out a five-step approach toward defining the most suitable methodology for the selection of portfolios from the numerous methods and techniques that have been discussed in current literature. Although most of the methods described in the prevailing literature take a project-centric approach toward portfolio evaluation and selection, the author attempts to articulate a more holistic view by emphasizing interdependencies between projects within a portfolio. Following this notion, five types of interdependencies are proposed, and methods and techniques for identifying and addressing these interdependencies are introduced. A second theme of this thesis is the adequate selection of metrics for both outcomes and process. Even though several debates exist in management science about how, how much and what to measure, little attention has been given to the measurement topic in association with project portfolio management. This is surprising, inasmuch as portfolio management can provide a qualitative and quantitative sanity check for the attainability of strategy, the need for resources and funds to implement certain strategic themes as well as other critical information. Rather than taking a prescriptive approach toward metrics, the author focuses on a simple metrics taxonomy and the tools to develop and evaluate metrics for their relevance, quality and viability. Lastly, this work discusses the reconciliation of potential misalignments between strategy and project portfolios, and achieving strategic alignment beyond the top-down view of strategic fit. The five propositions introduced by the author are validated with the help of a case study and a human subject experimen

Mission Dependency Index of Air Force Built Infrastructure: Knowledge Discovery with Machine Learning

Mission Dependency Index (MDI) is a metric developed to capture the relative criticality of infrastructure assets with respect to organizational missions. The USAF adapted the MDI metric from the United States Navy’s MDI methodology. Unlike the Navy’s MDI data collection process, the USAF adaptation of the MDI metric employs generic facility category codes (CATCODEs) to assign MDI values. This practice introduces uncertainty into the MDI assignment process with respect to specific missions and specific infrastructure assets. The uncertainty associated with USAF MDI values necessitated the MDI adjudication process. The MDI adjudication process provides a mechanism for installation civil engineer personnel to lobby for accurate MDI values for specific infrastructure assets. The MDI adjudication process requires manual identification of MDI discrepancies, documentation, and extensive coordination between organizations. Given the existing uncertainty with USAF MDI values and the effort required for the MDI adjudication process, this research pursues machine learning and the knowledge discovery in databases (KDD) process to identify and understand relationships between real property data and mission critical infrastructure. Furthermore, a decision support tool is developed for the MDI adjudication process. Specifically, supervised learning techniques are employed to develop a classifier that can identify potential MDI discrepancies. This automation effort serves to minimize the manual MDI review process by identifying a subset of facilities for potential adjudication

Entrepreneurial design of a digital health business

Software startups are an important source of innovation and wealth creation. Startups must develop and release software quickly to gain early cash flow and legitimacy for firm survival. They must search and identify a suitable market in a tight time frame. A ambiguity in the quality of the novel software designed can further intensify uncertainty for startups. Also, the entrepreneur-investor relationship is a critical conduit for financial and social resources, yet the investors’ influence on software design is understudied. This leads to the following research question: How does the entrepreneurial context in which the startup operates affect software design process and product in the early years? Given the exploratory nature of the research question, I drew on an eighteen-month, participant-observation case study of a three-year-old, digital healthcare startup, HealthCom. HealthCom designed a software to facilitate knowledge transfer between nurses and patients. I leveraged the attention-based view (ABV) as an organizing framework for analysis. By looking at how the environmental context influences the designers’ attention and actions, researchers can begin to understand the rationale behind design decisions. The findings illustrate how the attention of the designers were directed by financial-focused and quality-focused attention drivers. Financial-focused attention drivers originated from players (e.g. clients and investors) that intensified pressures for funding and led to the reframing of software as an enabler of funding. This contradicted quality-focused design alternatives grounded in user requirements and software design rules. It underlined the move from attention to designs that accommodated user requirements towards those that captured emerging business opportunities. Designers had to balance the tensions between financial and quality-focused design decisions that could lead to inhibition of future growth, compromises in quality or over-optimization of quality. Designers simplified and coupled requirements throughout the design process and postponed investments of design resources to meet client-imposed deadlines. The design product became disposable, quick to build, limited in adaptability, spartan, and vulnerable, and it enabled the startup to capture the business opportunities amidst time constraints, financial pressures, and high uncertainty.Information, Risk, and Operations Management (IROM

Semantics of trace relations in requirements models for consistency checking and inferencing

Requirements traceability is the ability to relate requirements back to stakeholders and forward to corresponding design artifacts, code, and test cases. Although considerable research has been devoted to relating requirements in both forward and backward directions, less attention has been paid to relating requirements with other requirements. Relations between requirements influence a number of activities during software development such as consistency checking and change management. In most approaches and tools, there is a lack of precise definition of requirements relations. In this respect, deficient results may be produced. In this paper, we aim at formal definitions of the relation types in order to enable reasoning about requirements relations. We give a requirements metamodel with commonly used relation types. The semantics of the relations is provided with a formalization in first-order logic. We use the formalization for consistency checking of relations and for inferring new relations. A tool has been built to support both reasoning activities. We illustrate our approach in an example which shows that the formal semantics of relation types enables new relations to be inferred and contradicting relations in requirements documents to be determined. The application of requirements reasoning based on formal semantics resolves many of the deficiencies observed in other approaches. Our tool supports better understanding of dependencies between requirements

Design approaches in technology enhanced learning

Design is a critical to the successful development of any interactive learning environment (ILE). Moreover, in technology enhanced learning (TEL), the design process requires input from many diverse areas of expertise. As such, anyone undertaking tool development is required to directly address the design challenge from multiple perspectives. We provide a motivation and rationale for design approaches for learning technologies that draws upon Simon's seminal proposition of Design Science (Simon, 1969). We then review the application of Design Experiments (Brown, 1992) and Design Patterns (Alexander et al., 1977) and argue that a patterns approach has the potential to address many of the critical challenges faced by learning technologists