Industrially Applicable System Regression Test Prioritization in Production Automation

When changes are performed on an automated production system (aPS), new faults can be accidentally introduced in the system, which are called regressions. A common method for finding these faults is regression testing. In most cases, this regression testing process is performed under high time pressure and on-site in a very uncomfortable environment. Until now, there is no automated support for finding and prioritizing system test cases regarding the fully integrated aPS that are suitable for finding regressions. Thus, the testing technician has to rely on personal intuition and experience, possibly choosing an inappropriate order of test cases, finding regressions at a very late stage of the test run. Using a suitable prioritization, this iterative process of finding and fixing regressions can be streamlined and a lot of time can be saved by executing test cases likely to identify new regressions earlier. Thus, an approach is presented in this paper that uses previously acquired runtime data from past test executions and performs a change identification and impact analysis to prioritize test cases that have a high probability to unveil regressions caused by side effects of a system change. The approach was developed in cooperation with reputable industrial partners active in the field of aPS engineering, ensuring a development in line with industrial requirements. An industrial case study and an expert evaluation were performed, showing promising results.Comment: 13 pages, https://ieeexplore.ieee.org/abstract/document/8320514

An Approach for Guiding Developers to Performance and Scalability Solutions

This thesis proposes an approach that enables developers who are novices in software performance engineering to solve software performance and scalability problems without the assistance of a software performance expert. The contribution of this thesis is the explicit consideration of the implementation level to recommend solutions for software performance and scalability problems. This includes a set of description languages for data representation and human computer interaction and a workflow

Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test Case Selection for Regression Testing

582-592The regression testing is principally carried out on modified parts of the programs. The quality of programs is the only concern of regression testing in the case of produced software. Main challenges to select mutant test cases are related to the affected classes. In software regression testing, the identification of optimal mutant test case is another challenge. In this research work, an evolutionary approach multi objective ant-lion optimization (MOALO) is proposed to identify optimal mutant test cases. The selection of mutant test cases is processed as multi objective enhancement problem and these will solve through MOALO algorithm. Optimal identification of mutant test cases is carried out by using the above algorithm which also enhances the regression testing efficiency. The proposed MOALO methods are implemented and tested using the Mat Lab software platform. On considering the populace size of 100, at that point the fitness estimation of the proposed framework, NSGA, MPSO, and GA are 3, 2.4, 1, and 0.3 respectively. The benefits and efficiencies of proposed methods are compared with random testing and existing works utilizing NSGA-II, MPSO, genetic algorithms in considerations of test effort, mutation score, fitness value, and time of execution. It is found that the execution times of MOALO, NSGA, MPSO, and GA are 2.8, 5, 6.5, and 7.8 respectively. Finally, it is observed that MOALO has higher fitness estimation with least execution time which indicates that MOALO methods provide better results in regression testing

Multi-Objective ANT Lion Optimization Algorithm Based Mutant Test CaseSelection for Regression Testing

The regression testing is principally carried out on modified parts of the programs. The quality of programs is the only concern of regression testing in the case of produced software. Main challenges to select mutant test cases are related to the affected classes. In software regression testing, the identification of optimal mutant test case is another challenge. In this research work, an evolutionary approach multi objective ant-lion optimization (MOALO) is proposed to identify optimal mutant test cases. The selection of mutant test cases is processed as multi objective enhancement problem and these willsolve through MOALO algorithm. Optimal identification of mutant test cases is carried out by using the above algorithm which also enhances the regression testing efficiency. The proposed MOALO methods are implemented and tested using the Mat Lab software platform. On considering the populace size of 100, at that point the fitness estimation of the proposed framework, NSGA, MPSO, and GA are 3, 2.4, 1, and 0.3 respectively. The benefits and efficiencies of proposed methods are compared with random testing and existing works utilizing NSGA-II, MPSO, genetic algorithms in considerations of test effort, mutation score, fitness value, and time of execution. It is found that the execution times of MOALO, NSGA, MPSO, and GA are 2.8, 5, 6.5, and 7.8 respectively. Finally, it is observed that MOALO has higher fitness estimation with least execution time which indicates that MOALO methods provide better results in regression testing

Selection and prioritization of organic contaminants for monitoring in the drinking water value chain

The occurrence of organic contaminants in the drinking water value chain (from source to tap) is a growing concern for the Drinking Water industry and its consumers given the high risk these contaminants can cause to the general public. These adverse health effects include such as endocrine disruption, toxicity teratogenicity, mutagenicity and carcinogenicity. Some of these organic contaminants are included in national and international drinking water quality guidelines or standards. However, although there are similarities in the list of organic contaminants used by each organization or country, the organic contaminants are never the same given the local conditions. There are also noticeable differences in the concentration limits set as targets or criteria for organic contaminants for public health protection via the use of drinking water. A further question requiring the response from drinking water regulators was whether the standards listed in the international literature would be applicable in other countries like South Africa. Complicating this decision is the fact that the South African National Drinking Water Standard (SANS 241) does not adequately address this component of drinking water quality management. The current standard only provides for dissolved organic carbon (DOC), total trihalomethanes (TTHMs) and phenols. However, the standard contains a statement which specifies that if there is a known organic contaminant, that may pose a health threat, it should be included in the monitoring programme and evaluated against World Health Organization (WHO) guidelines. To safeguard Drinking Water industry customers, it was deemed necessary to investigate this matter and establish a tool to assist with the identification of a list of organic contaminants to be monitored in the drinking water value chain. To achieve this a specific procedure/protocol needed to be developed, hence the aim of this study which was to develop a generic protocol for the selection and prioritization of organic contaminants for monitoring in the drinking water value chain (from source to tap). To achieve this, a critical evaluation and synthesis of the available literature on the approaches for the selection and prioritization of organic variables of priority to the drinking water industry was undertaken as a first step. From the literature review it was evident that there are currently many selection and prioritization approaches which are characterized mainly by the purpose for which the exercise has been conducted for. Approaches that prioritize chemicals according to their importance as environmental contaminants have been developed by government agencies and private industries such as the Health Canada’s Canadian Environmental Protection Agency (CEPA), the United Kingdom’s Institute for Environmental Health (IEH), the European Community’s Oslo and Paris (OSPAR) convention exercise for the protection of the Northeast Atlantic marine environment and the European Union (EU)’s combined monitoring based and modelling based priority setting scheme (EU-COMMPs). A few approaches such as ones published by the United States Environmental Protection Agency (USEPA), address the needs of the Drinking Water industry and there is no generic approach to the selection, prioritization and monitoring of organic contaminants in the drinking water value chain. From the review of selection and prioritization approaches, a generic model was developed. The model consists of three main steps, the compilation of a “pool of organic contaminants, the selection of relevant parameters and criteria to screen organic contaminants and finally the application of criteria to select priority organic contaminants. It was however realized that these steps were not enough if the protocol to be develop will serve its purpose. Selection and prioritization approaches are typically intended to be fairly simple and quick methods for determining the health and environmental hazards posed by the use and release of chemical substances into different environmental systems. This was taken into account during the development of the current protocol. Understanding that a protocol is a predefined written procedural method in the design and implementation of tasks and that these protocols are written whenever it is desirable to standardize a method or procedure to ensure successful reproducibility in a similar set up, a generic protocol was developed based on the model. The protocol developed in this study, operates as a multidisciplinary contaminants management and proactive protocol, thus exchanges toxicological, water quality, agricultural, chemical and public health information. The protocol uses previous or readily available information as a point of departure. It seeks to address the challenge facing the water industry in managing the current and emerging organic contaminants that are relevant to public health protection via the use of drinking water. Once the protocol was developed, it was validated in a prototype drinking water value chain. The exercise comprised of testing each step of the protocol from the selection of the “pool of organic contaminants (Step I) to recommending the final priority list of organic contaminants (Step VII). The implementation was successfully conducted in the Rand Water drinking water value chain. Emphasis of expert judgment was made as each step was validated and the opinion of key stakeholders used to shape the process. During Step III of the protocol, an intensive literature review was conducted to determine organic contaminants that have been identified in ground and surface water systems across the world. As a result of this review, major groups of organic contaminants that have been found to occur in source water resources across the world were identified. The identified groups of organic contaminants include, pesticides, polynuclear aromatic hydrocarbons, per and polyfluoroorganic compounds, polycyclic aromatic hydrocarbons, alkanes and alkenes, C10-C13 Chloroalkanes, pharmaceuticals and personal care products [PPCPs], surfactants, benzotriazoles, cyanotoxins and Carbon-based engineered nanoparticles. The risk profile of the identified organic contaminants was established using the persistence, bio-accumulation and toxicity criteria and the development of water quality monographs as an information dissemination tool. A conceptual framework for the implementation of the protocol by water utilities and relevant institutions has been developed from the experiences learnt during the validation exercise and a priority list of organic contaminants for the monitoring in the drinking water value chain to be used by Rand Water and other water utilities was identified. Some of the organic contaminants on this are currently being analyzed for in The Rand Water’s routine organic monitoring programme. During the validation exercise, the following were noted, During the identification of the “pool of organic contaminants” from the consulted information sources such as the WHO guidelines for drinking water quality, Health Canada drinking water quality guidelines, the USEPA drinking water quality standards, the New Zealand drinking water quality standards, USEPA IRIS database, the PAN-UK list of registered pesticides for South Africa, the IARC list for recognized carcinogens and the Department of Agriculture pesticides manuals duplications were observed. The time allocated could not allow for the development of water quality monographs for all organic contaminants of concern but for a few selected contaminants whose information was inadequate to allow for decision-making. The determination of concentration levels of organic contaminants in fish, sediment and water samples could have been limited by the failure of current analytical instruments to go down to lower levels at which they occur in the drinking water value chain. Only two events could be planned, during the wet season (high flow) and dry season (low flow) based on time and budget constraints. Although various experts were consulted and invited to attend workshops in order to validate the process, the attendance could not be extended to all nine provinces given the time and budget constraints. Based on the above, recommendations were made for the dissemination and use of the products emanating from this study. For example, it is recommended that the current protocol be made available to water utilities and the process of revising the current priority list be repeated every 5 years. Further research should be conducted to obtain full coverage of organic contaminants impacting on source water quality in all ground water and surface water systems used as sources for drinking water production. Another major recommendation is the investigation of potential analytical methods that current chromatographic methods with high resolution mass spectrometry to ensure that organic contaminants can be detected at the ng/l to pg/l using a single enrichment method in order to make sure that those organic contaminants that occur at very low concentration in environmental samples can be detected. For example, the realisation that compounds such as synthetic organic polymer residues, emerging disinfectant by-products, detergent metabolites, chlorinated benzenes, alkyl phenol, polyethoxylates, their metabolites and cyanotoxins are continuously discharged into the environment via wastewater and industrial effluent discharges which increases their concentration in aquatic environment and concomitantly their potential to exert adverse health effects in water used as source for the production of drinking water necessitates that each of these groups be added to the current monitoring programme. The current water quality monographs can be used for the benefit of the Drinking Water industry. It is also recommended that a training manual on the production and use of water quality monographs is produced to facilitate their dissemination. CD-ROMs on the water quality monographs can be produced and distributed with the manual.Thesis (PhD)--University of Pretoria, 2010.School of Health Systems and Public Health (SHSPH)PhDUnrestricte

Measuring circularity in food supply chain using life cycle assessment : refining oil from olive kernel

Valorization of food waste is a potential strategy toward a circular food supply chain. In this regard, measuring the circularity of food waste valorization systems is highly important to better understand multiple environmental impacts. Therefore, this study investigated the circularity of a food waste valorization system (refining oil from olive kernel) using a life cycle assessment methodology. An inventory of an industrial-based olive kernel oil production system is also provided in this study. The system boundary was the cradle to the factory gate of the production system. The results indicated that natural gas consumption was the highest contributor to most of the investigated impact categories. The global warming potential of one kg of oil produced from olive kernel was calculated to be 1.37 kg CO(2)eq. Moreover, the calculated damages of 1 kg oil production from olive kernel to human health, ecosystem quality, and resource depletion were 5.29 x 10(-7) DALY, 0.12 PDF center dot m(2)center dot yr., and 24.40 MJ, respectively

An Approach for Guiding Developers to Performance and Scalability Solutions

The quality of enterprise software applications plays a crucial role for the satisfaction of the users and the economic success of the enterprises. Software applications with unsatisfying performance and scalability are perceived by its users as low in quality, as less interesting and less attractive, and cause frustration when preventing the users from attaining their goals. This book proposes an approach for a recommendation system that enables developers who are novices in software perfor

Computer Aided Synthesis Prediction to Enable Augmented Chemical Discovery and Chemical Space Exploration

The drug-like chemical space is estimated to be 10 to the power of 60 molecules, and the largest generated database (GDB) obtained by the Reymond group is 165 billion molecules with up to 17 heavy atoms. Furthermore, deep learning techniques to explore regions of chemical space are becoming more popular. However, the key to realizing the generated structures experimentally lies in chemical synthesis. The application of which was previously limited to manual planning or slow computer assisted synthesis planning (CASP) models. Despite the 60-year history of CASP few synthesis planning tools have been open-sourced to the community. In this thesis I co-led the development of and investigated one of the only fully open-source synthesis planning tools called AiZynthFinder, trained on both public and proprietary datasets consisting of up to 17.5 million reactions. This enables synthesis guided exploration of the chemical space in a high throughput manner, to bridge the gap between compound generation and experimental realisation. I firstly investigate both public and proprietary reaction data, and their influence on route finding capability. Furthermore, I develop metrics for assessment of retrosynthetic prediction, single-step retrosynthesis models, and automated template extraction workflows. This is supplemented by a comparison of the underlying datasets and their corresponding models. Given the prevalence of ring systems in the GDB and wider medicinal chemistry domain, I developed ‘Ring Breaker’ - a data-driven approach to enable the prediction of ring-forming reactions. I demonstrate its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. Additionally, I highlight its potential for incorporation into CASP tools, and outline methodological improvements that result in the improvement of route-finding capability. To tackle the challenge of model throughput, I report a machine learning (ML) based classifier called the retrosynthetic accessibility score (RAscore), to assess the likelihood of finding a synthetic route using AiZynthFinder. The RAscore computes at least 4,500 times faster than AiZynthFinder. Thus, opens the possibility of pre-screening millions of virtual molecules from enumerated databases or generative models for synthesis informed compound prioritization. Finally, I combine chemical library visualization with synthetic route prediction to facilitate experimental engagement with synthetic chemists. I enable the navigation of chemical property space by using interactive visualization to deliver associated synthetic data as endpoints. This aids in the prioritization of compounds. The ability to view synthetic route information alongside structural descriptors facilitates a feedback mechanism for the improvement of CASP tools and enables rapid hypothesis testing. I demonstrate the workflow as applied to the GDB databases to augment compound prioritization and synthetic route design

A STUDY ON GENERAL ASSEMBLY LINE BALANCING MODELING METHODS AND TECHNIQUES

The borders of the assembly line balancing problem, as classically drawn, are as clear as any other operations research topic in production planning, with well-defined sets of assumptions, parameters, and objective functions. In application, however, these borders are frequently transgressed. Many of these deviations are internal to the assembly line balancing problem itself, arising from any of a wide array of physical or technological features in modern assembly lines. Other issues are founded in the tight coupling of assembly line balancing with external production planning and management problems, as assembly lines are at the intersection of multiple related problems in job sequencing, part flow logistics, worker safety, and quality. The field of General Assembly Line Balancing is devoted to studying the class of adapted and extended solution techniques necessary in order to model these applied line balancing problems. In this dissertation a complex line balancing problem is presented based on the real production environment of our industrial partner, featuring several extensions for task-to-task relationships, station characteristics limiting assignment, and parallel worker zoning interactions. A constructive heuristic is developed along with two improvement heuristics, as well as an integer programming model for the same problem. An experiment is conducted testing each of these new solution methods upon a battery of testbed problems, measuring solution quality, runtime, and achievement of feasibility. Additionally, a new method for measuring a secondary horizontal line balancing objective is established, based on the options-mix paradigm rather than the customary model-mix paradigm