Software evolution prediction using seasonal time analysis: a comparative study

Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse’s change request tracking system since the project’s start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests’ evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data

Management issues in systems engineering

When applied to a system, the doctrine of successive refinement is a divide-and-conquer strategy. Complex systems are sucessively divided into pieces that are less complex, until they are simple enough to be conquered. This decomposition results in several structures for describing the product system and the producing system. These structures play important roles in systems engineering and project management. Many of the remaining sections in this chapter are devoted to describing some of these key structures. Structures that describe the product system include, but are not limited to, the requirements tree, system architecture and certain symbolic information such as system drawings, schematics, and data bases. The structures that describe the producing system include the project's work breakdown, schedules, cost accounts and organization

Management quality management processes in a naval ship construction company: A qualitative case analysis

This industrial research study investigates the challenges encountered in the quality management implementation in a naval ship construction and maintenance company. This study will provide a proper view of the work completed in the process of ship construction and maintenance, especially in the Quality Department and will recommend improvements in quality, particularly in the building of a ship. Specifically, it aims to deeply examine the application of quality management knowledge and tools in the process-based work inspection planning, work monitoring activities and record-keeping information management. Additionally, the role of the Quality Department in the ship construction projects has been the main focus of this research study. In achieving the objectives, this case study has applied the qualitative approach which involved activities such as interviewing the focus group especially in Quality Department, observing the work-related activities that involve with quality work inspection process and reviewing quality-related documentation base on the ship construction work report and quality work inspection activities. The results of the three approaches were then triangulated and analysed by using Nvivo sohare for identification of relevant themes that normally use by qualitative researcher. The study has revealed the importance of team work and high understanding among various departments in managing the inspection planning and work-related information. It has identified the issues that had occurred in during the research, provided an analysis that can benefit the company and contributed to academic knowledge and also enhance the company's vision and mission. Furthermore, with proper improvement activities aligned with the actual work process will also result in higher productivity and quality of work processes as well as reducing the difficulties and problems encountered in the implementation of the quality management of this company

Knowledge Management as a Strategy & Competitive Advantage: A Strong Influence to Success (A Survey of Knowledge Management Case Studies of Different Organizations)

There has been a great deal of recognition in the business world that information and knowledge management can be vital tools in organizations. Knowledge management can be proven a competitive advantage of any organization. The rationale of this exploratory study is to investigate the link among knowledge management system & techniques and organizational success by using knowledge as completive advantage. It is a qualitative research study of different case studies of the use of knowledge management as competitive advantage in different organization that leads to success. A total of 8 different organizations are studied and results propose that by using knowledge management as strategy and competitive advantage, these organizations earn high profit. And it has a great influence to success. Implication and Directions are also discussed together with limitation and suggestions for future research. Keywords: Knowledge Management, Organization, Tacit Knowledge, Explicit Knowledge, KMS, KM Strategies, KM Technologies, Productivity, Competitive Advantage

An Experimental Study on Attribute Validity of Code Quality Evaluation Model

Regarding the practicality of the quality evaluation model, the lack of quantitative experimental evaluation affects the effective use of the quality model, and also a lack of effective guidance for choosing the model. Aiming at this problem, based on the sensitivity of the quality evaluation model to code defects, a machine learning-based quality evaluation attribute validity verification method is proposed. This method conducts comparative experiments by controlling variables. First, extract the basic metric elements; then, convert them into quality attributes of the software; finally, to verify the quality evaluation model and the effectiveness of medium quality attributes, this paper compares machine learning methods based on quality attributes with those based on text features, and conducts experimental evaluation in two data sets. The result shows that the effectiveness of quality attributes under control variables is better, and leads by 15% in AdaBoostClassifier; when the text feature extraction method is increased to 50 - 150 dimensions, the performance of the text feature in the four machine learning algorithms overtakes the quality attributes; but when the peak is reached, quality attributes are more stable. This also provides a direction for the optimization of the quality model and the use of quality assessment in different situations

Design for Producibility in Fabricated Aerospace Components - A framework for predicting and controlling geometrical variation and weld quality defects during multidisciplinary design

In the aerospace industry, weight reduction has been one of the key factors in making aircraft more fuel efficient in order to satisfy environmental demands and increase competitiveness. One strategy adopted by aircraft component suppliers to reduce weight has been fabrication, in which small cast or forged parts are welded together into a final shape. Fabrication increases design freedom due to the possibility of configuring several materials and geometries, which broadens out the design space and allows multioptimization in product weight, performance quality and cost. However, with fabrication, the number of assembly steps and the complexity of the manufacturing process have increased. The use of welding has brought to the forefront important producibility problems related to geometrical variation and weld quality.The goal of this research is to analyze the current situation in industry and academia and propose methods and tools within Engineering Design and Quality Engineering to solve producibility problems involving welded high performance integrated components. The research group “Geometry Assurance and Robust Design” at Chalmers University of Technology, in which this thesis has been produced, has the objective to simulate and foresee geometrical quality problems during the early phases of the product realization process to allow the development of robust concepts and the optimization of tolerances, thus solving producibility problems. Virtual manufacturing is a key within the multidisciplinary design process of aerospace components, in which automated processes analyze broad sets of design variants to trade-off requirements among various disciplines. However, as studied in this thesis, existing methods and tools to analyze producibility do not cover all aspects that define the quality of welded structures. Furthermore, to this day, not all phenomena related to welding can be virtually modelled. Understanding causes and effects still relies on expert judgements and physical experimentation to a great deal. However, when it comes to assessing the capability of many geometrical variants, such an effort might be costly. This deficiency indicates the need for virtual assessment methods and systematic experimentation to analyze the producibility of the design variants and produce process capability data that can be reused in future projects.To fulfill that need, this thesis provides support to designers in assessing producibility by virtually and rapidly predicting the welding quality of a large number of product design variants during the multidisciplinary design space process of fabricated aerospace components.The first step has been to map the fabrication process during which producibility problems might potentially occur. The producibility conceptual model has been proposed to represent the fabrication process in order to understand how variation is originated and propagated. With this representation at hand, a number of methods have been developed and employed to provide support to: 1) Identify and 2) Measure what affects producibility; 3) Analyze the effect of the interaction between factors that affect producibility and 4)Predict producibility. These activities and methods constitute the core of the proposed Design for Producibility framework. This framework combines specialized information about welding problems (know-hows), and inspection, testing and simulation data to systematically predict and evaluate the welding producibility of a set of product design variants. Through this thesis, producibility evaluations are no longer limited to a single geometry and the study of the process parameter window. Instead, a set of geometrical variants within the design space can be analyzed. The results can be used to perform optimization and evaluate trade-offs among different disciplines during design space exploration and analysis, thus supporting the multidisciplinary design process of fabricated (welded) aerospace components

Wiley Interdiscip Rev Comput Stat

Forecasts support decision making in a variety of applications. Statistical models can produce accurate forecasts given abundant training data, but when data is sparse or rapidly changing, statistical models may not be able to make accurate predictions. Expert judgmental forecasts-models that combine expert-generated predictions into a single forecast-can make predictions when training data is limited by relying on human intuition. Researchers have proposed a wide array of algorithms to combine expert predictions into a single forecast, but there is no consensus on an optimal aggregation model. This review surveyed recent literature on aggregating expert-elicited predictions. We gathered common terminology, aggregation methods, and forecasting performance metrics, and offer guidance to strengthen future work that is growing at an accelerated pace.R35 GM119582/GM/NIGMS NIH HHSUnited States/U01 IP001122/IP/NCIRD CDC HHSUnited States/2022-03-01T00:00:00Z33777310PMC799632111017vault:3684