Despite advancements in IDE tooling, code understanding, generation, and automated repair, debugging continues to present significant challenges. Existing debugging strategies available to developers in literature are often too mechanical and rigid for day-to-day issues. Recent advances in Large Language Models (LLMs) promise practical solutions that allow for more free-form debugging strategies. While LLMs offer satisfactory assistance in some cases, they often leap to action without sufficient context, making implicit assumptions and providing inaccurate responses. Moreover, the dialogue between developers and LLMs predominantly takes the form of question-answer pairs, placing the burden of formulating the correct questions and sustaining multi-turn conversations on the developer. We introduce Robin, a novel multi-agent conversational AI-assistant within GitHub Copilot Chat, specifically designed for debugging. Robin moves beyond the question-answer pairs by introducing the investigate & respond pattern, that focuses on using information gathered automatically from the IDE or gathered interactively from the developer before responding. Robin incorporates a general debugging strategy to systematically analyze bugs to sustain collaborative interactions while ensuring that the conversation does not deviate from the debugging task at hand. Through a within-subjects user study with 16 industry professionals, we find that equipping Robin to-(1) leverage the insert expansion interaction pattern, (2) facilitate turn-taking, and (3) utilize debugging strategies-leads to lowered conversation barriers, a 2.5 x improvement in bug localization and a substantial 3.5x improvement in bug resolution compared to AI-assisted debugging in Visual Studio prior to Robin.

We present a new skeletal representation along with a matching framework to address the deformable shape recognition problem. The disconnectedness arises as a result of excessive regularization that we use to describe a shape at an attainably coarse scale. Our motivation is to rely on stable properties the shape instead of inaccurately measured secondary details. The new representation does not suffer from the common instability problems of the traditional connected skeletons, and the matching process gives quite successful results on a diverse database of 2D shapes. An important difference of our approach from the conventional use of skeleton is that we replace the local coordinate frame with a global Euclidean frame supported by additional mechanisms to handle articulations and local boundary deformations. As a result, we can produce descriptions that are sensitive to any combination of changes in scale, position, orientation and articulation, as well as invariant ones.

We present a new axis-based shape representation scheme along with a matching framework to address the problem of generic shape recognition. The main idea is to define the relative spatial arrangement of local symmetry axes and their metric properties in a shape centered coordinate frame. The resulting descriptions are invariant to scale, rotation, small changes in viewpoint and articulations. Symmetry points are extracted from a surface whose level curves roughly mimic the motion by curvature. By increasing the amount of smoothing on the evolving curve, only those symmetry axes that correspond to the most prominent parts of a shape are extracted. The representation does not suffer from the common instability problems of the traditional connected skeletons. It captures the perceptual qualities of shapes well. 'Therefore finding the similarities and the differences among shapes becomes easier. The matching process gives highly successful results on a diverse database of 2D shapes.