6 citations · 7 across the 10 of their papers we have counts for
11 papers
The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE
Robert Feldt, Per Lenberg, Julian Frattini +1
AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engineering. Foundation models get…
LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study
Shuai Wang, Yinan Yu, Earl Barr +1
Multidisciplinary Software Development (MSD) requires domain experts and developers to collaborate across incompatible formalisms and separate artifact sets. Today, even with AI co…
DomAgent: Leveraging Knowledge Graphs and Case-Based Reasoning for Domain-Specific Code Generation
Shuai Wang, Dhasarathy Parthasarathy, Robert Feldt +1
Large language models (LLMs) have shown impressive capabilities in code generation. However, because most LLMs are trained on public domain corpora, directly applying them to real-…
Polymer: Development Workflows as Software
Dhasarathy Parthasarathy, Yinan Yu, Earl T. Barr
Software development builds digital tools to automate processes, yet its initial phases, up to deployment, remain largely manual. There are two reasons: Development tasks are often…
GateLens: A Reasoning-Enhanced LLM Agent for Automotive Software Release Analytics
Arsham Gholamzadeh Khoee, Shuai Wang, Robert Feldt +2
Ensuring reliable data-driven decisions is crucial in domains where analytical accuracy directly impacts safety, compliance, or operational outcomes. Decision support in such domai…
Automating a Complete Software Test Process Using LLMs: An Automotive Case Study
Shuai Wang, Yinan Yu, Robert Feldt +1
Vehicle API testing verifies whether the interactions between a vehicle's internal systems and external applications meet expectations, ensuring that users can access and control v…
GoNoGo: An Efficient LLM-based Multi-Agent System for Streamlining Automotive Software Release Decision-Making
Arsham Gholamzadeh Khoee, Yinan Yu, Robert Feldt +3
Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to…
Measuring design compliance using neural language models -- an automotive case study
Dhasarathy Parthasarathy, Cecilia Ekelin, Anjali Karri +2
As the modern vehicle becomes more software-defined, it is beginning to take significant effort to avoid serious regression in software design. This is because automotive software…
SilGAN: Generating driving maneuvers for scenario-based software-in-the-loop testing
Dhasarathy Parthasarathy, Anton Johansson
Automotive software testing continues to rely largely upon expensive field tests to ensure quality because alternatives like simulation-based testing are relatively immature. As a…
Does the dataset meet your expectations? Explaining sample representation in image data
Dhasarathy Parthasarathy, Anton Johansson
Since the behavior of a neural network model is adversely affected by a lack of diversity in training data, we present a method that identifies and explains such deficiencies. When…
Controlled time series generation for automotive software-in-the-loop testing using GANs
Dhasarathy Parthasarathy, Karl Bäckström, Jens Henriksson +1
Testing automotive mechatronic systems partly uses the software-in-the-loop approach, where systematically covering inputs of the system-under-test remains a major challenge. In cu…