Responsible AI and Analytics for an Ethical and Inclusive Digitized Society

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GraphEvo: Evaluating Software Evolution Using Machine Learning Based Call Graph Analytics And Network Portrait Divergence

Title from PDF of title page, viewed September 9, 2022Dissertation advisor: Yugyung LeeVitaIncludes bibliographical references (pages 151-168)Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 2022Understanding software evolution is essential for software development tasks, including debugging, maintenance, and testing. Unfortunately, as software changes, it becomes more prominent and more complicated, which makes it harder to understand. Software Defect Prediction (SDP) in the codebase is one of the most common ways artificial intelligence (AI) is used to improve the quality of agile products. But graph-based software metrics are seldom used in the software. In this dissertation, we propose a graph-based software framework called GraphEvo based on deep learning modeling for graphs. We applied the recent network comparison advancement to software networks via information theory-based metric Network portrait divergence (NPD). NPD captures the structural changes to call graph-based software networks. The NPD-based method determines what significant software changes are, how much execution paths are affected, and how tests are improved concerning the code. All of these factors affect how reliable the software is. To ensure that NPD-based software works well, version controls and Pull Requests (PRs) are used. GraphEvo's most significant contributions are: (i) Find and show how software has changed over time using call graphs. (ii) Using a machine learning and deep learning techniques to understand the software and guess how many defects are in each code entity (such as a class). (iii) Use the NPD-based tooling to create a public bug dataset and machine learning to see how well it can predict software defects. (iv) Help with the PR review process by knowing how the changes to code and tests that go with them work. We compared the performance of GraphEvo (i) across 66 software releases from five popular Java open-source systems to show that it works, (ii) for 9 Java projects and deep learning to make an SDP model, (iii) for 19 Java projects of different sizes and types from GitHub and to add bug information from other places, and (iv) for 627 PRs from 14 Java projects to see how vital tests are in PRs. These comprehensive experiments show that GraphEvo works well for debugging, maintaining, and testing software. We also received favorable responses from user studies, in which we asked software developers and testers what they thought of GraphEvo.Introduction -- Characterizing and understanding software evolution using call graphs -- Defect prediction using deep learning with NPD for software evolution -- NPD-based tooling, extendible defect dataset and its assessment -- Reviewing pull requests with path-based NPD and test

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Evaluation of the ingestive behaviour of the dairy cow under two systems of rotation with slope

The ingestive behaviour of grazing animals is modulated by the vegetation characteristics, topography and the type of stocking method. This research was carried out in 2019, at the Rumipamba CADER-UCE. It aimed to evaluate the impact of two contrasting stocking methods of dairy cows grazing a pasture with an average of slope >8.5%. Four dairy cows were set to graze a 0.4 ha paddock for 5 days for continuous stocking methods, while for the electric fence methods the dairy cows were restricted to 0.2 ha and the fence was moved uphill every 3 hours, repeating this process four times a day. Cow were equipped with activity sensors for 12 h per day. The whole procedure was repeated 2 times after realizing an equalization cuts and both paddocks, a rest time of 30 days and a random reassignment of paddocks to one of the treatments. The cows showed a difference in terms of the percentage of grazing P=0.0072, being higher with the electric fence (55% of the measurement time). From rising-plate-meter estimates of available biomass along the grazing periods, we calculated despite similar forage allowances (electric fence = 48.06 kg DM/cow/d and continuous = 48.21 DM/cow/d) a higher forage intake was obtained in the electric fence treatment (17.5 kg DM/cow/d) compared the continuous stocking (15.7 kg DM/cow/d) (P=0.006). In terms of milk production animals grazing under the differences electrical fence stocking method tended (P=0.0985) to produce more milk (17.39 kg/d) than those grazing in the continuous system (15.16 kg/d) due to the influence of the slope (P=0.05), while for milk quality the protein content was higher for the electric fence (33.7 g/l) than the continuous method (30.5 g/l) (P=0.039). None of the other milk properties differed between methods (P>0.05)