2 citations · 2 across the 9 of their papers we have counts for
6 papers · 1 filter
Does In-IDE Calibration of Large Language Models work at Scale?
Roham Koohestani, Agnia Sergeyuk, David Gros +4
The introduction of large language models into integrated development environments (IDEs) is revolutionizing software engineering, yet it poses challenges to the usefulness and rel…
Code4MeV2: a Research-oriented Code-completion Platform
Roham Koohestani, Parham Bateni, Aydin Ebrahimi +3
The adoption of AI-powered code completion tools in software development has increased substantially, yet the user interaction data produced by these systems remain proprietary wit…
HyperSeq: A Hyper-Adaptive Representation for Predictive Sequencing of States
Roham Koohestani, Maliheh Izadi
In the rapidly evolving world of software development, the surge in developers' reliance on AI-driven tools has transformed Integrated Development Environments into powerhouses of…
Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Unified Approach for Elevating Benchmark Quality
Roham Koohestani, Philippe de Bekker, Begüm Koç +1
Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for…
Rethinking IDE Customization for Enhanced HAX: A Hyperdimensional Perspective
Roham Koohestani, Maliheh Izadi
As Integrated Development Environments (IDEs) increasingly integrate Artificial Intelligence, Software Engineering faces both benefits like productivity gains and challenges like m…
Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests
Amirhossein Deljouyi, Roham Koohestani, Maliheh Izadi +1
Automated unit test generators, particularly search-based software testing tools like EvoSuite, are capable of generating tests with high coverage. Although these generators allevi…