Related papers: Presentation Proposal: Towards Efficient Data-flow…
Despite its ever-increasing impact, security is not considered as a design objective in commercial electronic design automation (EDA) tools. This results in vulnerabilities being overlooked during the software-hardware design process.…
While recent advances in large language models (LLMs) have shown promise in automating test generation for regression testing, they often suffer from limited reasoning about program execution, resulting in stagnated coverage growth - a…
Lean processes focus on doing only necessery things in an efficient way. Artificial intelligence and Machine Learning offer new opportunities to optimizing processes. The presented approach demonstrates an improvement of the test process by…
High data quality is critical for reliable analytics and operational efficiency. A growing ecosystem of tools has emerged to support data quality management, ranging from lightweight open-source libraries to comprehensive enterprise…
Data is a cornerstone of empirical software engineering (ESE) research and practice. Data underpin numerous process and project management activities, including the estimation of development effort and the prediction of the likely location…
The majority of cloud providers offers users the possibility to deploy Trusted Execution Environments (TEEs) to protect their data and processes from high privileged adversaries. This offer is intended to address concerns of users when…
Assessing and improving the quality of data are fundamental challenges for data-intensive systems that have given rise to applications targeting transformation and cleaning of data. However, while schema design, data cleaning, and data…
The arrival of recent cybersecurity standards has raised the bar for security assessments in organizations, but existing techniques don't always scale well. Threat analysis and risk assessment are used to identify security threats for new…
Static data-flow analysis has proven its effectiveness in assessing security of applications. One major challenge it faces is scalability to large software. This issue is even exacerbated when additional limitations on computing and storage…
Experimental science is increasingly driven by instruments that produce vast volumes of data and thus a need to manage, compute, describe, and index this data. High performance and distributed computing provide the means of addressing the…
Much of Software Engineering (SE) research assumes that progress depends on massive datasets and CPU-intensive optimizers. Yet has this assumption been rigorously tested? The counter-evidence presented in this paper suggests otherwise. For…
Software systems have been continuously evolved and delivered with high quality due to the widespread adoption of automated tests. A recurring issue hurting this scenario is the presence of flaky tests, a test case that may pass or fail…
Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and…
Access to longitudinal, individual-level data on work-life balance and wellbeing is limited by privacy, ethical, and logistical constraints. This poses challenges for reproducible research, methodological benchmarking, and education in…
We introduce QualityFlow, a dynamic agentic workflow for program synthesis. Given the English description of a programming problem and a set of unit tests, the model's goal is to synthesize the correct program that solves the problem and…
Register Transfer Level (RTL) design validation is a crucial stage in the hardware design process. We present a new approach to enhancing RTL design validation using available software techniques and tools. Our approach converts the source…
Models of software systems are used throughout the software development lifecycle. Dataflow diagrams (DFDs), in particular, are well-established resources for security analysis. Many techniques, such as threat modelling, are based on DFDs…
Data lakes have emerged as a flexible and scalable solution for storing and analyzing large volumes of heterogeneous data, including structured, semi-structured, and unstructured formats. Despite their growing adoption in both industry and…
Automatic test data generation (ATG) is a major topic in software engineering. In this paper, we seek to bridge the gap between the coverage criteria supported by symbolic ATG tools and the most advanced coverage criteria found in the…
In testing of software and Internet of Things (IoT) systems, one of necessary type of tests has to verify the consistency of data that are processed and stored in the system. The Data Cycle Test technique can effectively do such tests. The…