Harmonizing CMMI-DEV 1.2 and XP Method to Improve The Software Development Processes in Small Software Development Firms

Most software development organizations are small firms, and they have realized the need to manage and improve their software development and management activities. Traditional Software Process Improvement (SPI) models and standards are not realistic for these firms because of high cost, limited resources and strict project deadlines. Therefore, these firms need a lightweight software development method and an appropriate SPI model to manage and improve their software development and management processes. This study aims to construct a suitable software development process improvement framework for Small Software Development Firms (SSDFs) based on eXtreme Programming (XP) method and Capability Maturity Model Integration for Development Version 1.2 (CMMI-Dev1.2) model. Four stages are involved in developing the framework: (1) aligning XP practices to the specific goals of CMMI-Dev1.2 Key Process Areas (KPAs); (2) developing the proposed software development process improvement framework based on extending XP method by adapting the Extension-Based Approach (EBA), CMMI-Dev1.2, and generic elements of the SPI framework; (3) verifying the compatibility of the proposed framework to the KPAs of CMMI-Dev1.2 by using focus group method coupled with Delphi technique; and (4) validating the modified framework by using CMMI-Dev1.2 questionnaire as a main item to validate the suitability of the modified framework for SSDFs, and conducting two case studies to validate the applicability and effectiveness of this framework for these firms. The result of aligning XP practices to the KPAs of CMMI-Dev1.2 shows that twelve KPAs are largely supported by XP practices, eight KPAs are partially supported by XP practices, and two KPAs are not-supported by XP practices. The main contributions of this study are: software development process improvement framework for SSDFs, elicit better understanding of how to construct the framework, and quality improvement of the software development processes. There are possible avenues for extending this research to fulfil the missing specific practices of several KPAs, examining other agile practices and using CMMI-Dev1.3 to improve the framework, and conducting more case studie

Load Index Metrics for an Optimized Management of Web Services: A Systematic Evaluation

The lack of precision to predict service performance through load indices may lead to wrong decisions regarding the use of web services, compromising service performance and raising platform cost unnecessarily. This paper presents experimental studies to qualify the behaviour of load indices in the web service context. The experiments consider three services that generate controlled and significant server demands, four levels of workload for each service and six distinct execution scenarios. The evaluation considers three relevant perspectives: the capability for representing recent workloads, the capability for predicting near-future performance and finally stability. Eight different load indices were analysed, including the JMX Average Time index (proposed in this paper) specifically designed to address the limitations of the other indices. A systematic approach is applied to evaluate the different load indices, considering a multiple linear regression model based on the stepwise-AIC method. The results show that the load indices studied represent the workload to some extent; however, in contrast to expectations, most of them do not exhibit a coherent correlation with service performance and this can result in stability problems. The JMX Average Time index is an exception, showing a stable behaviour which is tightly-coupled to the service runtime for all executions. Load indices are used to predict the service runtime and therefore their inappropriate use can lead to decisions that will impact negatively on both service performance and execution cost

Quality of Context in Context-Aware Systems

Context-aware Systems (CASs) are becoming increasingly popular and can be found in the areas of wearable computing, mobile computing, robotics, adaptive and intelligent user interfaces. Sensors are the corner stone of context capturing however, sensed context data are commonly prone to imperfection due to the technical limitations of sensors, their availability, dysfunction, and highly dynamic nature of environment. Consequently, sensed context data might be imprecise, erroneous, conflicting, or simply missing. To limit the impact of context imperfection on the behavior of a context-aware system, a notion of Quality of Context (QoC) is used to measure quality of any information that is used as context information. Adaptation is performed only if the context data used in the decision-making has an appropriate quality level. This paper reports an analytical review for state of the art quality of context in context-aware systems and points to future research directions

Proceedings of the 9th Annual International Conference of the International Institute for Infrastructure Renewal and Reconstruction

Proceedings of The 9th Annual International Conference of the International Institute for Infrastructure Renewal and Reconstruction. The conference was held at Queensland University of Technology (QUT), Brisbane, Australia from 8-10 July 2013. The event title for the 9th Annual Conference was: Risk-informed Disaster Management: Planning for Response, Recovery and Resilience. All papers were double blind peer reviewed and the Proceedings were published online in March 2015

Copyright law, digital content and the internet in the Asia-Pacific

This e-book provides an insight into the key issues facing copyright law and digital content policy in a networked information world, based on papers presented at the First International Forum on the Content Industry and Intellectual Property. Published by Sydney University Press, this e-book provides an insight into the key issues facing copyright law and digital content policy in a networked information world, based on papers presented at the First International Forum on the Content Industry and Intellectual Property. The book features chapters from a wide range of experts in their respective fields from across the Asia-Pacific region, including Australia, the People\u27s Republic of China, Hong Kong, Indonesia and Singapore. Some of the areas examined include the new digital environment, digital content policy, the networked information economy, copyright law and new media. The book provides a timely and scholarly appraisal of the legal and policy considerations facing anyone trying to regulate, sponsor or utilise the vast array of new media and content platforms now available

On Provably Correct Decision-Making for Automated Driving

The introduction of driving automation in road vehicles can potentially reduce road traffic crashes and significantly improve road safety. Automation in road vehicles also brings several other benefits such as the possibility to provide independent mobility for people who cannot and/or should not drive. Many different hardware and software components (e.g. sensing, decision-making, actuation, and control) interact to solve the autonomous driving task. Correctness of such automated driving systems is crucial as incorrect behaviour may have catastrophic consequences. Autonomous vehicles operate in complex and dynamic environments, which requires decision-making and planning at different levels. The aim of such decision-making components in these systems is to make safe decisions at all times. The challenge of safety verification of these systems is crucial for the commercial deployment of full autonomy in vehicles. Testing for safety is expensive, impractical, and can never guarantee the absence of errors. In contrast, formal methods, which are techniques that use rigorous mathematical models to build hardware and software systems can provide a mathematical proof of the correctness of the system. The focus of this thesis is to address some of the challenges in the safety verification of decision-making in automated driving systems. A central question here is how to establish formal verification as an efficient tool for automated driving software development.A key finding is the need for an integrated formal approach to prove correctness and to provide a complete safety argument. This thesis provides insights into how three different formal verification approaches, namely supervisory control theory, model checking, and deductive verification differ in their application to automated driving and identifies the challenges associated with each method. It identifies the need for the introduction of more rigour in the requirement refinement process and presents one possible solution by using a formal model-based safety analysis approach. To address challenges in the manual modelling process, a possible solution by automatically learning formal models directly from code is proposed