XSearch: Explainable Code Search via Concept-to-Code Alignment

• URL: http://arxiv.org/abs/2605.16046v1
• Date: Fri, 15 May 2026 15:17:43 GMT
• Title: XSearch: Explainable Code Search via Concept-to-Code Alignment
• Abstract summary: We propose XSearch, an intrinsically explainable code search framework.<n>XSearch identifies functional concepts in the query and explicitly aligns them with corresponding code statements.<n>We train an encoder with explicit concept-alignment objectives and perform retrieval through explicit matching between query concepts and code statements.
• Score: 30.606443667402335
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic code search has been widely adopted in both academia and industry. These approaches embed natural-language queries and code snippets into a shared embedding space and retrieve results based on vector similarity. Despit strong performance on benchmark datasets, they often suffer from poor explainability and generalization. Retrieved code may appear semantically similar yet miss critical functional requirements of the query, while providing no explanation of why the result was retrieved. Moreover, such failures become more severe under distribution shift, where models struggle to generalize to unseen benchmarks. In this work, we propose XSearch, an intrinsically explainable code search framework. Our key insight is that by relying on global embedding similarity, existing retrievers inherently take an inductive view. They learn statistical patterns rather than truly understanding the query's functional requirements. We address this problem by reformulating code search as a deductive concept alignment problem. XSearch (i) identifies functional concepts in the query and (ii) explicitly aligns them with corresponding code statements. This explain-then-predict design produces inherent concept-level explanations and mitigates shortcut learning that harms out-of-distribution generalization. We train an encoder with explicit concept-alignment objectives and perform retrieval through explicit matching between query concepts and code statements. Experiments show that, trained on CodeSearchNet using GraphCodeBERT (125M parameters), XSearch improves performance on out-of-distribution benchmarks from 0.02 to 0.33 (15x) over eight state-of-the-art retrievers, and consistently outperforms both encoder- and decoder-based baselines with up to 7B parameters. A user study demonstrates that concept-alignment explanations enable users to evaluate retrieved results faster and more accurately.

Related papers

• CORE-Bench: A Comprehensive Benchmark for Code Retrieval in the Era of Agentic Coding [44.75033190929879]
  CORE-Bench is a benchmark for code retrieval in the era of agentic coding.<n>It evaluates ability at three levels: code understanding, issue-to-edit localization, and broader context retrieval.<n> Experiments with representative embedding models show a sharp drop from traditional code search to code retrieval in agentic coding settings.
  arXiv  Detail & Related papers  (2026-06-10T09:38:41Z)
• ROG: Retrieval-Augmented LLM Reasoning for Complex First-Order Queries over Knowledge Graphs [14.25887925588904]
  We propose a retrieval-augmented framework that combines query-aware neighborhood retrieval with large language model (LLM) chain-of-thought reasoning.<n>ROG decomposes a multi-operator query into a sequence of single-operator sub-queries.<n> Intermediate answer sets are cached and reused across steps, improving consistency on deep reasoning chains.
  arXiv  Detail & Related papers  (2026-02-02T17:45:43Z)
• AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion [55.21541958868449]
  We propose AlignCoder, a repository-level code completion framework.<n>Our framework generates an enhanced query that bridges the semantic gap between the initial query and the target code.<n>We employ reinforcement learning to train an AlignRetriever that learns to leverage inference information in the enhanced query for more accurate retrieval.
  arXiv  Detail & Related papers  (2026-01-27T15:23:14Z)
• Beyond Function-Level Search: Repository-Aware Dual-Encoder Code Retrieval with Adversarial Verification [11.965887077524577]
  We introduce RepoAlign-Bench, the first benchmark designed to evaluate repository-level code retrieval under change request driven scenarios.<n>We propose ReflectCode, an adversarial reflection augmented dual-tower architecture featuring disentangled code_encoder and doc_encoder components.<n>Experiments demonstrate that ReflectCode achieves 12.2% improvement in Top-5 Accuracy and 7.1% in Recall over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-10-16T18:47:04Z)
• RANGER -- Repository-Level Agent for Graph-Enhanced Retrieval [0.38298911754746934]
  General-purpose automated software engineering (ASE) includes tasks such as code completion, retrieval, repair, QA, and summarization.<n>These tasks require a code retrieval system that can handle specific queries about code entities, or code entity queries.<n>We present RANGER, a repository-level code retrieval agent designed to address both query types.
  arXiv  Detail & Related papers  (2025-09-27T18:57:02Z)
• Reasoning-enhanced Query Understanding through Decomposition and Interpretation [87.56450566014625]
  ReDI is a Reasoning-enhanced approach for query understanding through Decomposition and Interpretation.<n>We compiled a large-scale dataset of real-world complex queries from a major search engine.<n> Experiments on BRIGHT and BEIR demonstrate that ReDI consistently surpasses strong baselines in both sparse and dense retrieval paradigms.
  arXiv  Detail & Related papers  (2025-09-08T10:58:42Z)
• BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval [54.54576644403115]
  We introduce BRIGHT, the first text retrieval benchmark that requires intensive reasoning to retrieve relevant documents.<n>Our dataset consists of 1,384 real-world queries spanning diverse domains, such as economics, psychology, mathematics, and coding.<n>We show that incorporating explicit reasoning about the query improves retrieval performance by up to 12.2 points.
  arXiv  Detail & Related papers  (2024-07-16T17:58:27Z)
• Improving Code Search with Hard Negative Sampling Based on Fine-tuning [15.341959871682981]
  We introduce a cross-encoder architecture for code search that jointly encodes the concatenation of query and code. We also introduce a Retriever-Ranker (RR) framework that cascades the dual-encoder and cross-encoder to promote the efficiency of evaluation and online serving.
  arXiv  Detail & Related papers  (2023-05-08T07:04:28Z)
• Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting [80.9896041501715]
  Explanations that have not been "tuned" for a task, such as off-the-shelf explanations written by nonexperts, may lead to mediocre performance. This paper tackles the problem of how to optimize explanation-infused prompts in a blackbox fashion.
  arXiv  Detail & Related papers  (2023-02-09T18:02:34Z)
• Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation [50.14232079160476]
  We propose a new approach with multimodal contrastive learning and soft data augmentation for code search. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages.
  arXiv  Detail & Related papers  (2022-04-07T08:49:27Z)
• CoSQA: 20,000+ Web Queries for Code Search and Question Answering [63.92224685262063]
  CoSQA dataset includes 20,604 labels for pairs of natural language queries and codes. We introduce a contrastive learning method dubbed CoCLR to enhance query-code matching. We show that evaluated on CodeXGLUE with the same CodeBERT model, training on CoSQA improves the accuracy of code question answering by 5.1%.
  arXiv  Detail & Related papers  (2021-05-27T15:37:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.