Prioritizing maintainability defects based on refactoring recommendations

Leveraging and acting on the results of static analysis is a well-known best practice. Static analysis also is an integral part of software quality assurance which is highlighted by the inclusion of static analysis results in software quality models like ColumbusQM and Quamoco. Although there are studies that explore if tools for static analysis are used and how they are configured, few publications explore the longitudinal effects of acting on static analysis results on software and on the evolution of software. Especially effects on quality criteria, e.g., software quality metrics, defects, or readability are missing. With our research, we will bridge this gap and measure the effects of static analysis on software quality evolution. We will measure the effect the removal of code that generates static analysis warnings has on software quality metrics. Furthermore, we will measure long term effects on external quality attributes, e.g., reported issues and defects. Finally, we want to predict false positives of static analysis warnings by training predictive models on our collected data.

Through analyzing quality measurements of software, we have defined a new software quality measurement - STI (software trouble index). Moreover, we have proved that residual CFD (customer found defect) and STI have very similar behaviors when the number of software rebuilds gets large. When the number of software rebuilds is small, STI gives a better software quality indication than does the number of CFDs.

Private Sector-led Urban Development Projects. Management, Partnerships and Effects in the Netherlands and the UK

The term practice development has been associated with supporting modernization, and organizational, service and quality improvements but more importantly in promoting patient-centredness. Originating in the UK, it is increasingly being adopted in other countries. Its perceived value is in its facilitative approach to engaging, empowering, enlightening, evolving and evaluating patients, users and professionals within the processes of innovation and change through collaboration, team working and partnership building. Despite the plethora of literature outlining the relative merits and demerits of practice development to the healthcare professions and professionals and health and social care organizations there is limited evidence substantiating the existence of a knowledge base within the field of practice development. This paper aims to present the underpinning evidence confirming the existence of a knowledge base within the field of practice development. An in-depth critical review and synthesis of the literature on CINAHL, undertaken at the end of 2004 using the key words ‘practice development’ revealed 416 articles. This was limited to 130, which made direct reference to practice development in the title. From these articles, 23 were research focused and 107 associated with informing the practice development debate. The in-depth critical review and synthesis of the literature demonstrated that practice development can be defined and deciphered to show its impact in supporting innovation and change to individual, team and organizational practice. Practice development's primary principles are centred on promoting patient-centredness through the utilization of a facilitative approach to team working, collaboration, and partnership building and by networking. By nature of design and virtue practice development is not linear but a messy concept, yet among the mess of daily practice it does adopt a systematic and organized holistic approach to advancing and evaluating practice. The in-depth critical review and synthesis of the practice development literature reinforces the need to consolidate a position in order to move forward rather than back. This review confirms that practice development is an emerging discipline within health and social care demonstrating an acquired body of knowledge and an evidence base to substantiate its case. By plotting its history and reviewing the current evidence base, it would be fair to state that practice development is here to stay and due to grow within all professional groups. Copyright © 2006 John Wiley & Sons, Ltd.

What does it mean to use a method? Towards a practice theory for software engineering

Size, complexity, and human dependency on software--based products have grown dramatically during past decades. Software developers are struggling to deliver reliable software with acceptable level of quality, within given budget and schedule. One measure of software quality and reliability is the number of residual faults. Therefore, researchers are focusing on the identification of the number of fault presents in the software or identification of program modules that are most likely to contain faults. A lot of models have been developed using various techniques. A common approach is followed for software reliability prediction utilizing failure data. Software reliability and quality prediction is highly desired by the stakeholders, developers, managers, and end users. Detecting software faults early during development will definitely improve the reliability and quality in cost-effective way.KeywordsSoftware ReliabilityFault PredictionSoftware MetricsCapability Maturity ModelTest Case PrioritizationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Software reliability has been considered as a crucial parameter in the measurement of software quality. In the present scenario, software companies try to develop high reliable software to sustain in the competitive market environment. Reliability may be defined in terms of software ability up to which it can perform its desired functions without any failure. Many software reliability growth models (SRGMs) have been provided by the researchers with a sole motive of reliability measurement. The high availability of these models raises a question that “Is each model is fit to each type of dataset?” To answer this question, a comprehensive analysis of SRGMs is highly required. The present study highlights the various concepts about the SRGMs so that they can be used more efficiently to measure the software reliability.

The number of people who need special care and are living alone has increased significantly in recent years. To help and assist them, different kinds of systems have been developed, for example, to detect and alert falls. However, the acceptance of such systems requires on the user part the belief that the system uses the collected data properly and not cause any harm. Specifically, trust for ubiquitous systems, which represent computing everywhere, anytime, and transparent for the final user, is a relevant issue. Thus, this paper evaluates, using measures of software quality, trust in a ubiquitous system, called fAlert, for detecting and warning falls. fAlert is an Android system that makes use of sensors to detect anomalies in everyday user activities.

Static analysis tools cannot detect violations of application-specific rules. They can be extended with specialized checkers that implement the verification of these rules. However, such rules are usually not documented explicitly. Moreover, the implementation of specialized checkers is a manual process that requires expertise. In this work, application-specific programming rules are automatically extracted from execution traces collected at runtime. These traces are analyzed offline to identify programming rules. Then, specialized checkers for these rules are introduced as extensions to a static analysis tool so that their violations can be checked throughout the source code. We implemented our approach for Java programs, considering 3 types of faults. We performed an evaluation with an industrial case study from the telecommunications domain. We were able to detect real faults with checkers that were generated based on the analysis of execution logs.

The purpose of this paper was to examine the importance of smart production planning for improving performance, considering the specific features of the planning environment in the pharmaceutical industry. This study uses four empirical case studies in the pharmaceutical industry. These four cases represent four companies producing different products within the pharmaceutical industry for various markets, each with distinct processes. Additionally, along with industry insights, these frameworks were used to identify, define, and structure the variables expected to have the greatest impact on the importance of smart production planning within the theoretical framework. This work was carried out through discussions within the project team and repeated multiple times. The framework was subsequently used to map and analyze the four empirical case studies in the biopharmaceutical pharmaceutical industry. The cases were selected to demonstrate different features of the planning environment and smart production planning approaches, ranging from make-to-stock (MTS) to assemble-to-order (ATO) and make-to-order (MTO). Data was collected through observations and site visits at pharmaceutical manufacturing facilities, workshops, consultations, and formal interviews with production managers, production planners, smart supply chain managers, innovation managers, and production line engineers. Internal and cross-case analyses were performed through several iterations within the pharmaceutical industry team. The fundamental finding is that the potential and importance of smart production planning for improving performance increase with the complexity of the production planning environment. As a result, nine final steps for the design and development of smart production planning importance are presented. The conceptual framework presented is based on the combination of smart production planning, the features of the production planning environment, and the elements of the Fourth Industrial Revolution. The developed framework serves two objectives. First, it provides a tool to structure and describe the planning environment in the pharmaceutical industry, considering product, market, and process features. Insights gained from such a framework regarding the planning environment's features can be useful for smart production purposes. The second contribution of the framework is linking the planning environment's features with smart production planning. The core logic is that the importance of smart production planning for improving performance increases with the intelligence and complexity of the planning environment.

Six Sigma in Project Management for Software Companies

: The Rome Air Development Center has developed the Software Quality Framework as a means to specify software quality goals and measure software quality. Much of the work to date has focused on metrics applicable to software code. This report describes an effort undertaken to measure the quality of software products earlier in the software development life cycle, during the design phase, and to automate the capture of metric data from design media. Metrics of software quality, primarily those related to the criterion simplicity (or conversely, complexity), were reviewed. This review includes those metrics previously developed in the Software Quality Framework. Two metrics, Halstead's Software Science and McCabe's Cyclomatic Complexity were chosen for their amenability to measurement during design and their potential for automation. Two design media were used: Design Aids for Real-Time Systems, an experimental automated design tool developed at the Charles Stark Draper Laboratory; and Ada as a program design language.

Industry is using more than 30% of the total energy consumed in industrialized countries and manufacturing is one of the most energy-intensive industrial activities. Because of the current global energy crisis, reducing energy consumption in manufacturing has become one of the top research priorities. This paper aims to review the existing technologies that directly improve the energy efficiency in manufacturing plant as well as identify future research opportunities. Four levels to study energy saving technologies are systems, process, equipment, and facility. Two industrial energy monitoring case studies are presented where idle energy is observed to contribute up to 63% of the total energy consumption.

In the 2015 Medicare Access and CHIP Reauthorization Act, the Centers for Medicare and Medicaid Services (CMS) began the transition to a value-based payment system through implementation of the Quality Payment Program (QPP) ([1][1]). Representing a paradigm shift away from traditional volume-based

All neurologists must begin incorporating quality measurement and quality improvement into their practice. Efforts to pay physicians based on the quality of their care and patient outcomes moves quality measurement beyond reporting to satisfy regulatory requirements and pushes physicians to select and use quality measures to improve patient outcomes and patient experience. This article provides practical steps and proposes considerations for neurologic practices advancing quality measurement and improvement.