The Real World Software Process

The industry-wide demand for rapid development in concert with greater process maturity has seen many software development firms adopt tightly structured iterative processes. While a number of commercial vendors offer suitable process infrastructure and tool support, the cost of licensing, configuration and staff training may be prohibitive for the small and medium size enterprises (SMEs) which dominate the Asia-Pacific software industry. This work addresses these problems through the introduction of the Real World Software Process (RWSP), a freely available, Web-based iterative scheme designed specifically for small teams and organisations. RWSP provides a detailed process description, high quality document templates - including code review and inspection guidelines - and the integrated tutorial support necessary for successful usage by inexperienced developers and teams. In particular it is intended that the process be readily usable by software houses which at present do not follow a formal process, and that the free RWSP process infrastructure should be a vehicle for improving industry standards

A Platform-Based Software Design Methodology for Embedded Control Systems: An Agile Toolkit

A discrete control system, with stringent hardware constraints, is effectively an embedded real-time system and hence requires a rigorous methodology to develop the software involved. The development methodology proposed in this paper adapts agile principles and patterns to support the building of embedded control systems, focusing on the issues relating to a system's constraints and safety. Strong unit testing, to ensure correctness, including the satisfaction of timing constraints, is the foundation of the proposed methodology. A platform-based design approach is used to balance costs and time-to-market in relation to performance and functionality constraints. It is concluded that the proposed methodology significantly reduces design time and costs, as well as leading to better software modularity and reliability

Between analysis and transformation: technology, methodology and evaluation on the SPLICE project

This paper concerns the ways in which technological change may entail methodological development in e-learning research. The focus of our argument centres on the subject of evaluation in e-learning and how technology can contribute to consensus-building on the value of project outcomes, and the identification of mechanisms behind those outcomes. We argue that a critical approach to the methodology of evaluation which harnesses technology in this way is vital to agile and effective policy and strategy-making in institutions as the challenges of transformation in a rapidly changing educational and technological environment are grappled with. With its focus on mechanisms and multiple stakeholder perspectives, we identify Pawson and Tilley’s ‘Realistic Evaluation’ as an appropriate methodological approach for this purpose, and we report on its use within a JISC-funded project on social software, SPLICE (Social Practices, Learning and Interoperability in Connected Environments). The project created new tools to assist the identification of mechanisms responsible for change to personal and institutional technological practice. These tools included collaborative mind-mapping and focused questioning, and tools for the animated modelling of complex mechanisms. By using these tools, large numbers of project stakeholders could engage in a process where they were encouraged to articulate and share their theories and ideas as to why project outcomes occurred. Using the technology, this process led towards the identification and agreement of common mechanisms which had explanatory power for all stakeholders. In conclusion, we argue that SPLICE has shown the potential of technologically-mediated Realistic Evaluation. Given the technologies we now have, a methodology based on the mass cumulation of stakeholder theories and ideas about mechanisms is feasible. Furthermore, the summative outcomes of such a process are rich in explanatory and predictive power, and therefore useful to the immediate and strategic problems of the sector. Finally, we argue that as well as generating better explanations for phenomena, the evaluation process can itself become transformative for stakeholders

Systems thinking and practice for action research

This chapter offers some grounding in systems thinking and practice for doing action research. There are different traditions within systems thinking and practice which, if appreciated, can become part of the repertoire for practice by action researchers. After exploring some of these lineages the differences between systemic and systematic thinking and practice are elucidated – these are the two adjectives that come from the word 'system', but they describe quite different understandings and practices. These differences are associated with epistemological awareness and distinguishing systemic action research from action research. Finally, some advantages for action research practice from engaging with systems thinking and practice are discussed

Managing the integration problem in concurrent engineering

Includes bibliographical references (p. 30-31).Funded by General Motors and the Leaders for Manufacturing Program, a partnership involving thirteen major U.S. manufacturing firms and M.I.T.'s Schools of Engineering and Management.Kent R. McCord and Steven D. Eppinger

A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters

Current LTE network is faced with a plethora of Configuration and Optimization Parameters (COPs), both hard and soft, that are adjusted manually to manage the network and provide better Quality of Experience (QoE). With 5G in view, the number of these COPs are expected to reach 2000 per site, making their manual tuning for finding the optimal combination of these parameters, an impossible fleet. Alongside these thousands of COPs is the anticipated network densification in emerging networks which exacerbates the burden of the network operators in managing and optimizing the network. Hence, we propose a machine learning-based framework combined with a heuristic technique to discover the optimal combination of two pertinent COPs used in mobility, Cell Individual Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio (SINR) of all the connected users. The first part of the framework leverages the power of machine learning to predict the KPI of interest given several different combinations of CIO and HOM. The resulting predictions are then fed into Genetic Algorithm (GA) which searches for the best combination of the two mentioned parameters that yield the maximum mean SINR for all users. Performance of the framework is also evaluated using several machine learning techniques, with CatBoost algorithm yielding the best prediction performance. Meanwhile, GA is able to reveal the optimal parameter setting combination more efficiently and with three orders of magnitude faster convergence time in comparison to brute force approach