Coding agents have rapidly emerged as the primary interface for AI-assisted software development. However, despite their growing adoption, relatively little is known about their internal architecture, and no systematic architectural description comparable to those available for compilers or operating systems currently exists. This paper addresses this gap by documenting the main architectural components of coding agents, explaining their responsibilities, interactions, and execution flow. To support this effort, we also present Ark (Agent Research Kit), a minimal open-source coding agent designed for research and education that preserves the essential architectural mechanisms of modern coding agents while emphasizing simplicity and clarity. We also introduce ArkBench, a lightweight benchmark comprising ten representative software maintenance and evolution tasks. Using gpt-5.4-mini, Ark successfully solved 8 of the 10 tasks while requiring modest token consumption. Finally, we compare the architecture of Ark with those of state-of-the-art coding agents using a recently proposed architectural taxonomy. We hope that both Ark and ArkBench provide a practical foundation for teaching, research, and experimentation on coding agents.
Property-Based Testing (PBT) automatically generates test inputs to validate properties of programs, shifting developers’ effort from writing examples to specifying invariants. While the technique has gained popularity in Python through the Hypothesis framework, little is known about how developers adopt and use it in practice. This paper reports on three empirical studies. First, we analyzed 367 PBTs from 244 Python projects, classifying them into nine property categories and quantifying their use of Hypothesis constructs. We found that Test Oracle properties dominate (29.97%), and that PBTs are generally concise (median 14 LOC), relying heavily on built-in strategies (75.20%), but also on external (22.62%) and internal (17.17%) ones. Second, we studied 213 Stack Overflow posts tagged with PBT, revealing that the main challenges developers face concern data generation strategies (36.62%), especially for composite and tabular data (24.36%). Finally, we evaluated Ghostwriter, Hypothesis’s automated test generator, against 203 tests from our dataset; only 18.23% were fully automatable, while most required partial adaptation (30.05%) or were incompatible (51.72%). Together, our findings provide the largest empirical characterization of PBT in Python to date, highlight developers’ difficulties in adopting the technique, and expose limitations of current tool support.
Isadora Campos De Oliveira, Arthur Lisboa Corgozinho, Henrique Rocha et al.· Empirical Software Engineeri...· 0 citations