170 research outputs found
Statistical Mutation Calling from Sequenced Overlapping DNA Pools in TILLING Experiments
No full textAbstract Background TILLING (Targeting induced local lesions IN genomes) is an efficient reverse genetics approach for detecting induced mutations in pools of individuals. Combined with the high-throughput of next-generation sequencing technologies, and the resolving power of overlapping pool design, TILLING provides an efficient and economical platform for functional genomics across thousands of organisms. Results We propose a probabilistic method for calling TILLING-induced mutations, and their carriers, from high throughput sequencing data of overlapping population pools, where each individual occurs in two pools. We assign a probability score to each sequence position by applying Bayes' Theorem to a simplified binomial model of sequencing error and expected mutations, taking into account the coverage level. We test the performance of our method on variable quality, high-throughput sequences from wheat and rice mutagenized populations. Conclusions We show that our method effectively discovers mutations in large populations with sensitivity of 92.5% and specificity of 99.8%. It also outperforms existing SNP detection methods in detecting real mutations, especially at higher levels of coverage variability across sequenced pools, and in lower quality short reads sequence data. The implementation of our method is available from: http://www.cs.ucdavis.edu/filkov/CAMBa/.</p
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Predicting Sustainability in Open-Source Projects: A Socio-Technical Approach using EPEX
No full textOpen-source projects are pivotal to the innovation and sustainability of software, yet their complex dynamics can be challenging to decipher. Building upon the foundational work of the DECAL group, which demonstrated the utility of socio-technical networks in analyzing the lifecycle of projects, and the APEX tool developed for Apache projects to forecast sustainability, this thesis extends the idea and introduces the Eclipse Project Explorer (EPEX) tool. Employing dual-network models—social and technical—this research explores open-source collaborations within the Eclipse ecosystem. The social network leverages data from mailing lists and other communication platforms to visualize interactions among developers, while the technical network utilizes GitHub commit data to trace file changes, illuminating the evolution of software projects. These networks are visualized over time, offering developers deeper insights into project lifecycles and interaction patterns. Furthermore, we introduce a groundbreaking ‘graduation forecast‘ model that predicts the long-term sustainability of projects based on analytical data. By applying our methodology to selected case studies within Eclipse, our findings not only validate the effectiveness of our approach but also enhance our understanding of project sustainability, providing a robust tool for strategic decision-making in software development. This thesis lays foundational work for future research into the lifecycle management of open-source software, significantly extending prior research
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Enabling Synthetic Data Usage for Medical Research
No full textAcquiring data can be a major hurdle to any data science problem. Sometimes there isn’t enough data or, as is particularly the case for healthcare data, it may contain sensitive information such as personal identifiers that should not be shared. By generating synthetic health data, researchers aim to overcome obstacles of data access and privacy concerns and thereby allow for quicker and broader use of data by the research community. Through this thesis I have surveyed the current state of synthetic data usage in medical research, recorded the thoughts, experiences, and opinions of synthetic data use in medical research from interviewing medical researchers, selected synthetic data generation tools, assessed the accessibility, usability, and efficacy of the selected data generation tool with the help of two different use case groups, experimented with creative ways to use the chosen synthetic data tool, and used my experiences to write resources for current and future researchers who need assistance getting started with synthetic data generation through the UC Davis DataLab
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Studying OSS Sustainability via Socio-technical Structure and Institutional Governance
No full textOpen Source Software (OSS) projects have become an integral part of our digital landscape, revolutionizing the way we develop, distribute, and consume software in various sectors. From operating systems to complex data analysis tools, OSS projects are the backbone of the global digital infrastructure. They offer a collaborative platform for developers worldwide, fostering the development and maintenance of high-quality software by leveraging the collective knowledge and expertise of a diverse, global community. However, despite their undeniable potential and benefits, OSS projects often face challenges related to sustainability, requiring effective governance, and a committed community of volunteering contributors. Understanding and addressing these challenges is crucial for maintaining the sustainability of OSS projects, ultimately benefiting the broader digital ecosystem.This thesis aims to investigate the dynamics of OSS projects to understand the underlying factors contributing to their sustainability or lack thereof. This investigation primarily provides insights into the following research questions: How effective can we predict sustainability based on socio-technical traces of OSS projects? Can we identify the determinants for OSS sustainability along with their weights and directions? And, are there temporal associations between socio-technical structure and institutional governance? Answers to the above questions can help us design tools and methodologies to forecast the sustainability trajectory of OSS projects. This would allow stakeholders, such as project managers, contributors, and sponsors, to make informed decisions about resource allocation, project involvement, and risk management
AI-driven Java Performance Testing: Balancing Result Quality with Testing Time
No full textPerformance testing aims at uncovering efficiency issues of software systems. In order to be both effective and practical, the design of a performance test must achieve a reasonable trade-off between result quality and testing time. This becomes particularly challenging in Java context, where the software undergoes a warm-up phase of execution, due to just-in-time compilation. During this phase, performance measurements are subject to severe fluctuations, which may adversely affect quality of performance test results. Both practitioners and researchers have proposed approaches to mitigate this issue. Practitioners typically rely on a fixed number of iterated executions that are used to warm-up the software before starting to collect performance measurements (state-of-practice). Researchers have developed techniques that can dynamically stop warm-up iterations at runtime (state-of-the-art). However, these approaches often provide suboptimal estimates of the warm-up phase, resulting in either insufficient or excessive warm-up iterations, which may degrade result quality or increase testing time. There is still a lack of consensus on how to properly address this problem. Here, we propose and study an AI-based framework to dynamically halt warm-up iterations at runtime. Specifically, our framework leverages recent advances in AI for Time Series Classification (TSC) to predict the end of the warm-up phase during test execution. We conduct experiments by training three different TSC models on half a million of measurement segments obtained from JMH microbenchmark executions. We find that our framework significantly improves the accuracy of the warm-up estimates provided by state-of-practice and state-of-the-art methods. This higher estimation accuracy results in a net improvement in either result quality or testing time for up to +35.3% of the microbenchmarks. Our study highlights that integrating AI to dynamically estimate the end of the warm-up phase can enhance the cost-effectiveness of Java performance testing
A High-level Architecture of an Automated Context-aware Ethics-based Negotiation Approach
No full textThis paper briefly outlines a high-level architecture of a context-aware ethics-based negotiation approach in which autonomous systems utilize user ethical profiles, together with contextual factors and user status, to control their autonomy while collaboratively negotiating to reach an ethical agreement that satisfies the ethical beliefs of all parties involved
Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning Approach
No full textProducing accurate software models is crucial in model-driven software engineering (MDE). However, modeling complex systems is an error-prone task that requires deep application domain knowledge. In the past decade, several automated techniques have been proposed to support academic and industrial practitioners by providing relevant modeling operations. Nevertheless, those techniques require a huge amount of training data that cannot be available due to several factors, e.g., privacy issues. The advent of large language models (LLMs) can support the generation of synthetic data although state-of-the-art approaches are not yet supporting the generation of modeling operations. To fill the gap, we propose a conceptual framework that combines modeling event logs, intelligent modeling assistants, and the generation of modeling operations using LLMs. In particular, the architecture comprises modeling components that help the designer specify the system, record its operation within a graphical modeling environment, and automatically recommend relevant operations. In addition, we generate a completely new dataset of modeling events by telling on the most prominent LLMs currently available. As a proof of concept, we instantiate the proposed framework using a set of existing modeling tools employed in industrial use cases within different European projects. To assess the proposed methodology, we first evaluate the capability of the examined LLMs to generate realistic modeling operations by relying on well-founded distance metrics. Then, we evaluate the recommended operations by considering real-world industrial modeling artifacts. Our findings demonstrate that LLMs can generate modeling events even though the overall accuracy is higher when considering human-based operations. In this respect, we see generative AI tools as an alternative when the modeling operations are not available to train traditional IMAs specifically conceived to support industrial practitioners
Self-elicitation of requirements with automated GUI prototyping
No full textRequirements Elicitation (RE) is a crucial activity especially in the early stages of software development. GUI prototyping has widely been adopted as one of the most effective RE techniques for user-facing software systems. However, GUI prototyping requires (i) the availability of experienced requirements analysts, (ii) typically necessitates conducting multiple joint sessions with customers and (iii) creates considerable manual effort. In this work, we propose SERGUI, a novel approach enabling the Self-Elicitation of Requirements (SER) based on an automated GUI prototyping assistant. SERGUI exploits the vast prototyping knowledge embodied in a large-scale GUI repository through Natural Language Requirements (NLR) based GUI retrieval and facilitates fast feedback through GUI prototypes. The GUI retrieval approach is closely integrated with a Large Language Model (LLM) driving the prompting-based recommendation of GUI features for the current GUI prototyping context and thus stimulating the elicitation of additional requirements. We envision SERGUI to be employed in the initial RE phase, creating an initial GUI prototype specification to be used by the analyst as a means for communicating the requirements. To measure the effectiveness of our approach, we conducted a preliminary evaluation. Video presentation of SERGUI at: https://youtu.be/pzAAB9Uht8
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