The Effect of Applying Design of Experiments Techniques to Software Performance Testing

Effective software performance testing is essential to the development and delivery of quality software products. Many software testing investigations have reported software performance testing improvements, but few have quantitatively validated measurable software testing performance improvements across an aggregate of studies. This study addressed that gap by conducting a meta-analysis to assess the relationship between applying Design of Experiments (DOE) techniques in the software testing process and the reported software performance testing improvements. Software performance testing theories and DOE techniques composed the theoretical framework for this study. Software testing studies (n = 96) were analyzed, where half had DOE techniques applied and the other half did not. Five research hypotheses were tested, where findings were measured in (a) the number of detected defects, (b) the rate of defect detection, (c) the phase in which the defect was detected, (d) the total number of hours it took to complete the testing, and (e) an overall hypothesis which included all measurements for all findings. The data were analyzed by first computing standard difference in means effect sizes, then through the Z test, the Q test, and the t test in statistical comparisons. Results of the meta-analysis showed that applying DOE techniques in the software testing process improved software performance testing (p \u3c 05). These results have social implications for the software testing industry and software testing professionals, providing another empirically-validated testing methodology. Software organizations can use this methodology to differentiate their software testing process, to create more quality products, and to benefit the consumer and society in general

Methodology for a Numerical Multidimensional Optimization of a Mixer Coupled to a Compressor for Its Integrationin a Hyperloop Vehicle

[EN] The current environmental concern has led both the industry and researchers to look for alternate means of transport. Amongst them, the hyperloop has become a quite promising idea. In order to overcome some of its limitations, including a compressor in its propulsive system has been investigated. In this paper, a strategy to improve the design of the mixer, which will blend the bypass and core streams coming out of the compressor, was addressed. Due to the lack of ad hoc compressors and the impossibility of experimental testing, a multidimensional optimization methodology with CFD tools was developed. A Taguchi DOE was employed for a preliminary 2D optimization from an initial geometry, whereas a numerical adjoint method was explored for the whole 3D mixer. By using this method, an initial decrease in the pressure drop of 16%16% was obtained with the 2D stage, whereas an additional 10%10% reduction was achieved in the 3D optimization. With this, the propulsive efficiency of the whole hyperloop system will be improved.Project supported by the "Agencia Valenciana de la Innovacio" and the European Regional Development Fund 2014-2020: INNEST/2021/221 and INNEST/2021/344. Borja Pallas was supported by "Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana DOCEMPR UPV program" from the regional government, Generalitat Valenciana co-funded by the European Social Fund.Galindo, J.; Dolz, V.; Navarro, R.; Pallás, B.; Torres, G. (2022). Methodology for a Numerical Multidimensional Optimization of a Mixer Coupled to a Compressor for Its Integrationin a Hyperloop Vehicle. Applied Sciences. 12(24):1-25. https://doi.org/10.3390/app122412795125122

Design of Experiments

This book is a research publication that covers original research on developments within the Design of Experiments - Applications field of study. The book is a collection of reviewed scholarly contributions written by different authors and edited by Dr. Messias Borges Silva. Each scholarly contribution represents a chapter and each chapter is complete in itself but related to the major topics and objectives. The target audience comprises scholars and specialists in the field

National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program, 1992, volume 1

The 1992 Johnson Space Center (JSC) National Aeronautics and Space Administration (NASA)/American Society for Engineering Education (ASEE) Summer Faculty Fellowship Program was conducted by the University of Houston and JSC. The program at JSC, as well as the programs at other NASA Centers, was funded by the Office of University Affairs, Washington, DC. The objectives of the program, which began nationally in 1964 and at JSC in 1965, are (1) to further the professional knowledge of qualified engineering and science faculty members; (2) to stimulate an exchange of ideas between participants and NASA; (3) to enrich and refresh the research and teaching activities of participants' institutions; and (4) to contribute to the research objective of the NASA Centers. This document is a compilation of the final reports 1 through 12

Systems design methodology for personalised design customisation of sports wheelchairs

The state of the art in current wheelchair sports equipment design, demonstrates a steady progression in the process of wheelchair design improvement and adaptation to purpose in order to suit different sporting disciplines and game roles. However, the process of tuning the design of the wheelchair to suit the particular needs of elite athletes in terms of performance and ergonomic requirements, currently requires a research and evidence-based design methodology. This investigation aims to: i) characterise the sports wheelchair activity, including performance variables and design parameters in relation to the three main classification groups of wheelchair rugby athletes (high, mid and low pointers); ii) investigate the rugby wheelchair performance indicators both under game and laboratory conditions and establish critical relationships between wheelchair design parameters and the performance requirements for elite wheelchair rugby athletes; iii) identify the relevant design space for design customisation of rugby wheelchairs for individual wheelchair rugby athletes and the contribution of each design parameter to athlete’s mobility performance; iv) formulate a systems design methodology for design customisation of rugby wheelchairs that can be used to determine a high performance wheelchair design that best matches the requirements of the individual athlete. In order to effectively refine the user interphase design of the wheelchair, it is essential to narrow down the key dimensions within the design space, that are likely to have an effect on the performance of an individual athlete. The methods developed and used throughout this thesis are grounded on a number of case studies built on analysis of the test data obtained from elite wheelchair rugby athletes who volunteer for this research project. Initially, the Qualitative Function Deployment (QFD) method is used to systematically analyse data collected through international online surveys, focus groups and questionnaires from elite level wheelchair rugby players. Subsequently, experimental studies conducted on court and in the laboratory yield significant relationships between wheelchair design parameters and task-specific performance functions. Four key design factors (wheel diameter, camber angle, seat height and camber bar depth) are then iterated at incremental dimensional levels to the athlete’s current chair configuration; and tests are performed and analysed through an new extension to the Taguchi method. Subsequent analyses of acceleration, velocity, and time in the push phase of the propulsion cycle, as well as recovery time for each of the participating athletes performing a linear sprint task are correlated to the positive/negative contribution of each of the four design factors to the outlined performance variables as well as their combined effect in a specific wheelchair configuration model. Performance rankings and magnitude-based inferences on the true value of the effect statistic are then used to define a high performance design space for athlete’s wheelchair selection. This process has led to the formulation of a systems design methodology for design customisation of sports wheelchairs, which can be used across the various sporting disciplines to customize wheelchairs that best meet the athlete's needs in terms of performance and ergonomic requirements

Improving project management planning and control in service operations environment.

Projects have evidently become the core activity in most companies and organisations where they are investing significant amount of resources in different types of projects as building new services, process improvement, etc. This research has focused on service sector in attempt to improve project management planning and control activities. The research is concerned with improving the planning and control of software development projects. Existing software development models are analysed and their best practices identified and these have been used to build the proposed model in this research. The research extended the existing planning and control approaches by considering uncertainty in customer requirements, resource flexibility and risks level variability. In considering these issues, the research has adopted lean principles for planning and control software development projects. A novel approach introduced within this research through the integration of simulation modelling techniques with Taguchi analysis to investigate ‗what if‘ project scenarios. Such scenarios reflect the different combinations of the factors affecting project completion time and deliverables. In addition, the research has adopted the concept of Quality Function Deployment (QFD) to develop an automated Operations Project Management Deployment (OPMD) model. The model acts as an iterative manner uses ‗what if‘ scenario performance outputs to identify constraints that may affect the completion of a certain task or phase. Any changes made during the project phases will then automatically update the performance metrics for each software development phases. In addition, optimisation routines have been developed that can be used to provide management response and to react to the different levels of uncertainty. Therefore, this research has looked at providing a comprehensive and visual overview of important project tasks i.e. progress, scheduled work, different resources, deliverables and completion that will make it easier for project members to communicate with each other to reach consensus on goals, status and required changes. Risk is important aspect that has been included in the model as well to avoid failure. The research emphasised on customer involvement, top management involvement as well as team members to be among the operational factors that escalate variability levels 3 and effect project completion time and deliverables. Therefore, commitment from everyone can improve chances of success. Although the role of different project management techniques to implement projects successfully has been widely established in areas such as the planning and control of time, cost and quality; still, the distinction between the project and project management is less than precise and a little was done in investigating different levels of uncertainty and risk levels that may occur during different project phase.United Arab Emirates Governmen

Investigation on selected factors causing variability in additive manufactured parts

Additive Manufacturing (AM) is known for its ability to manufacture complex parts layer by layer using 3D design data. AM brings significant freedom in design, yet it can get hard to produce the same parts with identical dimensional tolerances, a.k.a. reproducibility problem. Reproducibility, the ability to produce the same part again under same conditions, is one of the major challenges in AM as it plays an important role in the replacement of worn-out/damaged parts in an assembly. Ceramics, metals, alloys, and plastics are being used for the biomedical implants in which the concept of reproducibility is crucial. To obtain quality products and maintain consistency, this study is conducted to analyze the effects of most common and critical factors – layer thickness, printing speed, orientation angle on dimensional accuracy and surface roughness of AM parts. A full-factorial Design of Experiment (DOE) involving these factors with three levels each is implemented to determine their effect on overall length, height, width, middle height, and surface roughness, which are the response parameters. A dog-bone shaped tensile testing specimen is printed with Poly Lactic Acid (PLA) polymer using Fused Filament Fabrication (FFF) technology. Dimensional features and surface roughness of parts are then measured to determine the variability in output for different levels of input. The results of ANOVA analysis are used to conclude about the significant factors and their levels. The ANOVA results show that the response parameters are affected by main effects, 2-way interactions, and 3-way interactions in different combinations. The optimal conditions obtained from ANOVA analysis are used to print some more parts to plot control charts and conduct capability analysis. Control charts are used to monitor the process variability and capability analysis is conducted to check if the process is in statistical control and can produce parts within specifications. The small size of the parts allows the results of this study to be applicable in biomedical and industrial sectors. This study containing three input parameters with three levels each considers main effects along with interaction effects which have not been considered previously in our literature review. Also, the combination of factors is unique, and their effect combined has not been focused in previous studies

Human inspired pattern recognition via local invariant features

Vision is increasingly becoming a vital element in the manufacturing industry. As complex as it already is, vision is becoming even more difficult to implement in a pattern recognition environment as it converges toward the level of what humans visualize. Relevant brain work technologies are allowing vision systems to add capability and tasks that were long reserved for humans. The ability to recognize patterns like humans do is a good goal in terms of performance metrics for manufacturing activities. To achieve this goal, we created a neural network that achieves pattern recognition analogous to the human visual cortex using high quality keypoints by optimizing the scale space and pairing keypoints with edges as input into the model. This research uses the Taguchi Design of Experiments approach to find optimal values for the SIFT parameters with respect to finding correct matches between images that vary in rotation and scale. The approach used the Taguchi L18 matrix to determine the optimal parameter set. The performance obtained from SIFT using the optimal solution was compared with the performance from the original SIFT algorithm parameters. It is shown that correct matches between an original image and a scaled, rotated, or scaled and rotated version of that image improves by 17% using the optimal values of the SIFT. A human inspired approach was used to create a CMAC based neural network capable of pattern recognition. A comparison of 3 object, 30 object, and 50 object scenes were examined using edge and optimized SIFT based features as inputs and produced extensible results from 3 to 50 objects based on classification performance. The classification results prove that we achieve a high level of pattern recognition that ranged from 96.1% to 100% for objects under consideration. The result is a pattern recognition model capable of locally based classification based on invariant information without the need for sets of information that include input sensory data that is not necessarily invariant (background data, raw pixel data, viewpoint angles) that global models rely on in pattern recognition

A robust multi-objective statistical improvement approach to electric power portfolio selection

Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.PhDCommittee Chair: Mavris, Dimitri; Committee Member: Duncan, Scott; Committee Member: Ender, Tommer; Committee Member: German, Brian; Committee Member: Paredis, Chri