| 61. |
- Aeddula, Omsri, 1993-
(författare)
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Data-Driven Decision Support Systems for Product Development - A Data Exploration Study Using Machine Learning
- 2021
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern product development is a complex chain of events and decisions. The ongoing digital transformation of society, increasing demands in innovative solutions puts pressure on organizations to maintain, or increase competitiveness. As a consequence, a major challenge in the product development is the search for information, analysis, and the build of knowledge. This is even more challenging when the design element comprises complex structural hierarchy and limited data generation capabilities. This challenge is even more pronounced in the conceptual stage of product development where information is scarce, vague, and potentially conflicting. The ability to conduct exploration of high-level useful information using a machine learning approach in the conceptual design stage would hence enhance be of importance to support the design decision-makers, where the decisions made at this stage impact the success of overall product development process.The thesis aims to investigate the conceptual stage of product development, proposing methods and tools in order to support the decision-making process by the building of data-driven decision support systems. The study highlights how the data can be utilized and visualized to extract useful information in design exploration studies at the conceptual stage of product development. The ability to build data-driven decision support systems in the early phases facilitates more informed decisions.The thesis presents initial descriptive study findings from the empirical studies, showing the capabilities of the machine learning approaches in extracting useful information, and building data-driven decision support systems. The thesis initially describes how the linear regression model and artificial neural networks extract useful information in design exploration, providing support for the decision-makers to understand the consequences of the design choices through cause-and-effect relationships on a detailed level. Furthermore, the presented approach also provides input to a novel visualization construct intended to enhance comprehensibility within cross-functional design teams. The thesis further studies how the data can be augmented and analyzed to extract the necessary information from an existing design element to support the decision-making process in an oral healthcare context.
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| 62. |
- Aeddula, Omsri, 1993-, et al.
(författare)
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Image-Based Localization System
- 2020
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Ingår i: Proceedings of the 8th ICIECE 2019. - Singapore : Springer. ; , s. 535-541
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Konferensbidrag (refereegranskat)abstract
- The position of a vehicle is essential for navigation of the vehicle along the desired path without a human interference. A good positioning system should have both good positioning accuracy and reliability. Global Positioning System (GPS) employed for navigation in a vehicle may lose significant power due to signal attenuation caused by construction buildings or other obstacles. In this paper, a novel real-time indoor positioning system using a static camera is presented. The proposed positioning system exploits gradient information evaluated on the camera video stream to recognize the contours of the vehicle. Subsequently, the mass center of the vehicle contour is used for simultaneous localization of the vehicle. This solution minimizes the design and computational complexity of the positioning system. The experimental evaluation of the proposed approach has demonstrated the positioned accuracy of 92.26%. © Springer Nature Singapore Pte Ltd. 2020.
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| 63. |
- Aeddula, Omsri, 1993-
(författare)
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Navigating Data Challenges: AI-Driven Decision Support for Product-Service System Development
- 2024
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Solution providers are transitioning from product-centric models to service-oriented solutions. This shift has led to the rise of Product-Service Systems (PSS), which offer a holistic approach by integrating physical products with associated services. However, the inherent complexity and collaborative nature of PSS development present a significant challenge: information gathering, analysis, and knowledge building. This is further amplified in the early stages of PSS development due to data challenges such as uncertainty, ambiguity, and complexity. This complicates informed decision-making, potentially leading to the risk of sub-optimal outcomes and impacting the success of final offerings.This research proposes an AI-powered data analysis approach to address these data challenges and augment the decision-making process of PSS development. The focus is on supporting early-stage decision-making, as decisions made at this stage greatly impact the success of final solutions. The research investigates how data can be utilized and visualized to extract actionable insights, ultimately facilitating informed decision-making.The presented research demonstrates that AI-powered data analysis effectively supports informed decision-making in early-stage PSS development. By extracting actionable insights from complex data, handling data limitations, and enabling informed strategic decisions, knowledge sharing, and collaboration are facilitated among stakeholders. Furthermore, integrating AI with visualization tools fosters knowledge building and a deeper understanding of system behavior, ultimately leading to more successful PSS solutions. The efficacy of AI-powered data analysis for handling diverse data types across application domains is demonstrated, potentially leading to benefits such as a deeper understanding of system behavior and proactive solution strategies. These advancements contribute to developing decision support systems specifically for PSS development.Overall, this research demonstrates the efficacy of AI-powered data analysis in overcoming data challenges and empowering decision-makers in early-stage PSS development. This translates to more informed choices, leading to the creation of successful and efficient PSS solutions.
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| 64. |
- Afzal, Wasif, et al.
(författare)
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A Comparative Evaluation of Using Genetic Programming for Predicting Fault Count Data
- 2008
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Ingår i: Proceedings - The 3rd International Conference on Software Engineering Advances, ICSEA 2008, Includes ENTISY 2008: International Workshop on Enterprise Information Systems. - : IEEE. - 9780769533728 ; , s. 407-414
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Konferensbidrag (refereegranskat)abstract
- There have been a number of software reliability growth models (SRGMs) proposed in literature. Due to several reasons, such as violation of models' assumptions and complexity of models, the practitioners face difficulties in knowing which models to apply in practice. This paper presents a comparative evaluation of traditional models and use of genetic programming (GP) for modeling software reliability growth based on weekly fault count data of three different industrial projects. The motivation of using a GP approach is its ability to evolve a model based entirely on prior data without the need of making underlying assumptions. The results show the strengths of using GP for predicting fault count data.
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| 65. |
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| 66. |
- Afzal, Wasif, et al.
(författare)
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A systematic review of search-based testing for non-functional system properties
- 2009
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Ingår i: Information and Software Technology. - : Butterworth-Heinemann Newton, MA, USA. - 0950-5849 .- 1873-6025. ; 51:6, s. 957-976
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Tidskriftsartikel (refereegranskat)abstract
- Search-based software testing is the application of metaheuristic search techniques to generate software tests. The test adequacy criterion is transformed into a fitness function and a set of solutions in the search space are evaluated with respect to the fitness function using a metaheuristic search technique. The application of metaheuristic search techniques for testing is promising due to the fact that exhaustive testing is infeasible considering the size and complexity of software under test. Search-based software testing has been applied across the spectrum of test case design methods; this includes white-box (structural), black-box (functional) and grey-box (combination of structural and functional) testing. In addition, metaheuristic search techniques have also been applied to test non-functional properties. The overall objective of undertaking this systematic review is to examine existing work into non-functional search-based software testing (NFSBST). We are interested in types of non-functional testing targeted using metaheuristic search techniques, different fitness functions used in different types of search-based non-functional testing and challenges in the application of these techniques. The systematic review is based on a comprehensive set of 35 articles obtained after a multi-stage selection process and have been published in the time span 1996-2007. The results of the review show that metaheuristic search techniques have been applied for non-functional testing of execution time, quality of service, security, usability and safety. A variety of metaheuristic search techniques are found to be applicable for non-functional testing including simulated annealing, tabu search, genetic algorithms, ant colony methods, grammatical evolution, genetic programming (and its variants including linear genetic programming) and swarm intelligence methods. The review reports on different fitness functions used to guide the search for each of the categories of execution time, safety, usability, quality of service and security; along with a discussion of possible challenges in the application of metaheuristic search techniques.
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| 67. |
- Afzal, Wasif, et al.
(författare)
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An experiment on the effectiveness and efficiency of exploratory testing
- 2015
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Ingår i: Empirical Software Engineering. - : Springer. - 1382-3256 .- 1573-7616. ; 20:3, s. 844-878
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Tidskriftsartikel (refereegranskat)abstract
- The exploratory testing (ET) approach is commonly applied in industry, but lacks scientific research. The scientific community needs quantitative results on the performance of ET taken from realistic experimental settings. The objective of this paper is to quantify the effectiveness and efficiency of ET vs. testing with documented test cases (test case based testing, TCT). We performed four controlled experiments where a total of 24 practitioners and 46 students performed manual functional testing using ET and TCT. We measured the number of identified defects in the 90-minute testing sessions, the detection difficulty, severity and types of the detected defects, and the number of false defect reports. The results show that ET found a significantly greater number of defects. ET also found significantly more defects of varying levels of difficulty, types and severity levels. However, the two testing approaches did not differ significantly in terms of the number of false defect reports submitted. We conclude that ET was more efficient than TCT in our experiment. ET was also more effective than TCT when detection difficulty, type of defects and severity levels are considered. The two approaches are comparable when it comes to the number of false defect reports submitted.
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| 68. |
- Afzal, Wasif, et al.
(författare)
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Genetic programming for cross-release fault count predictions in large and complex software projects
- 2010
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Ingår i: Evolutionary Computation and Optimization Algorithms in Software Engineering. - : IGI Global, Hershey, USA. - 9781615208098
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Bokkapitel (refereegranskat)abstract
- Software fault prediction can play an important role in ensuring software quality through efficient resource allocation. This could, in turn, reduce the potentially high consequential costs due to faults. Predicting faults might be even more important with the emergence of short-timed and multiple software releases aimed at quick delivery of functionality. Previous research in software fault prediction has indicated that there is a need i) to improve the validity of results by having comparisons among number of data sets from a variety of software, ii) to use appropriate model evaluation measures and iii) to use statistical testing procedures. Moreover, cross-release prediction of faults has not yet achieved sufficient attention in the literature. In an attempt to address these concerns, this paper compares the quantitative and qualitative attributes of 7 traditional and machine-learning techniques for modeling the cross-release prediction of fault count data. The comparison is done using extensive data sets gathered from a total of 7 multi-release open-source and industrial software projects. These software projects together have several years of development and are from diverse application areas, ranging from a web browser to a robotic controller software. Our quantitative analysis suggests that genetic programming (GP) tends to have better consistency in terms of goodness of fit and accuracy across majority of data sets. It also has comparatively less model bias. Qualitatively, ease of configuration and complexity are less strong points for GP even though it shows generality and gives transparent models. Artificial neural networks did not perform as well as expected while linear regression gave average predictions in terms of goodness of fit and accuracy. Support vector machine regression and traditional software reliability growth models performed below average on most of the quantitative evaluation criteria while remained on average for most of the qualitative measures.
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| 69. |
- Afzal, Wasif, et al.
(författare)
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Incorporating Metrics in an Organizational Test Strategy
- 2008
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Ingår i: International Conference on Software Testing, Verification and Validation. - : IEEE. - 9780769533889 ; , s. 304-315
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Konferensbidrag (refereegranskat)abstract
- An organizational level test strategy needs to incorporate metrics to make the testing activities visible and available to process improvements. The majority of testing measurements that are done are based on faults found in the test execution phase. In contrast, this paper investigates metrics to support software test planning and test design processes. We have assembled metrics in these two process types to support management in carrying out evidence-based test process improvements and to incorporate suitable metrics as part of an organization level test strategy. The study is composed of two steps. The first step creates a relevant context by analyzing key phases in the software testing lifecycle, while the second step identifies the attributes of software test planning and test design processes along with metric(s) support for each of the identified attributes.
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| 70. |
- Afzal, Wasif, et al.
(författare)
-
Lessons from applying experimentation in software engineering prediction systems
- 2008
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Ingår i: Proceedings of The 2nd International workshop on Software Productivity Analysis and Cost Estimation (SPACE'08), Collocated with 15th Asia-Pacific Software Engineering Conference. - Beijing : State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences.
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Konferensbidrag (refereegranskat)abstract
- Within software engineering prediction systems, experiments are undertaken primarliy to investigate relationships and to measure/compare models' accuracy. This paper discusses our experience and presents useful lessons/guidelines in experimenting with software engineering prediction systems. For this purpose, we use a typical software engineering experimentation process as a baseline. We found that the typical software engineering experimentation process in software engineering is supportive in developing prediction systems and have highlighted issues more central to the domain of software engineering prediction systems.
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