Software fault localization is widely known to be costly, which motivates researchers to develop automated root cause analysis techniques. While many existing approaches focus on pinpointing the location of bugs, they often overlook the process of locating them. However, in practice, while the exact location or fix of a bug can be subjective, the explanation of how a bug manifests is often more objective and broadly agreed upon. Such explanations can thus offer a stronger foundation for understanding and resolving bugs. In this work, we propose, TraceWalker, a tool-supported root cause analysis approach that generates a step-by-step debugging plan to guide developers toward the root cause with convincing explanations. Technically, we build the execution trace as a causal model where each step, control dependency, and data dependency is modeled with quantified suspiciousness. Based on the suspiciousness, TraceWalker traverses the execution trace to guide users toward the root cause. Specifically, each travel is on an estimated decision to explore control or data dependencies on the trace. While guiding the programmers to the root cause, TraceWalker collects their agreement on the estimated decision as feedback, which in turn updates the predicted root cause with a new planned travelling path. Our simulation experiments on Defects4j dataset show that (1) TraceWalker is effective in root-cause location comparing to state-of-the-art approaches, (2) TraceWalker can generate accurate explanation and save the debugging efforts to reach the root cause and (3) TraceWalker is robust against potential incorrect feedbacks. Moreover, our user study on 21 participants show that TraceWalker can boost the debugging performance with convincing explanation in practice.

Recent years have seen the development of LLM-based code generation. Compared to generating code in a software project, incremental code edits are empirically observed to be more frequent. The emerging code editing approaches usually formulate the problem as generating an edit based on known relevant prior edits and context. However, practical code edits can be more complicated. First, an editing session can include multiple (ir)relevant edits to the code under edit. Second, the inference of the subsequent edits is non-trivial as the scope of its ripple effect can be the whole project. In this work, we propose CoEdPilot, an LLM-driven solution to recommend code edits by discriminating the relevant edits, exploring their interactive natures, and estimating its ripple effect in the project. Specifically, CoEdPilot orchestrates multiple neural transformers to identify what and how to edit in the project regarding both edit location and edit content. When a user accomplishes an edit with an optional editing description, an Subsequent Edit Analysis first reports the most relevant files in the project with what types of edits (e.g., keep, insert, and replace) can happen for each line of their code. Next, an Edit-content Generator generates concrete edit options for the lines of code, regarding its relevant prior changes reported by an Edit-dependency Analyzer. Last, both the Subsequent Edit Analysis and the Edit-content Generator capture relevant prior edits as feedback to readjust their recommendations. We train our models by collecting over 180K commits from 471 open-source projects in 5 programming languages. Our extensive experiments show that (1) CoEdPilot can well predict the edits (i.e., predicting edit location with accuracy of 70.8%-85.3%, and the edit content with exact match rate of 41.8% and BLEU4 score of 60.7); (2) CoEdPilot can well boost existing edit generators such as GRACE and CCT5 on exact match rate by 8.57% points and BLEU4 score by 18.08. Last, our user study on 18 participants with 3 editing tasks (1) shows that CoEdPilot can be effective in assisting users to edit code in comparison with Copilot, and (2) sheds light on the future improvement of the tool design. The video demonstration of our tool is available at https://sites.google.com/view/coedpilot/home.