C ode I nsight: A Curated Dataset of Practical Coding Solutions from S tack O verflow (2024)

Abstract: We introduce a novel dataset tailored for code generation, aimed at aiding developers in common tasks. Our dataset provides examples that include a clarified intent, code snippets associated, and an average of three related unit tests. It encompasses a range of libraries such as Pandas, Numpy, and Regex, along with more than 70 standard libraries in Python code derived from Stack Overflow. Comprising 3,402 crafted examples by Python experts, our dataset is designed for both model finetuning and standalone evaluation. To complete unit tests evaluation, we categorize examples in order to get more fine grained analysis, enhancing the understanding of models’ strengths and weaknesses in specific coding tasks. The examples have been refined to reduce data contamination, a process confirmed by the performance of three leading models: Mistral 7B, CodeLLAMA 13B, and Starcoder 15B. We further investigate data-contamination testing GPT-4 performance on a part of our dataset. The benchmark can be accessed at anonymized address.

## introduction in the dynamic landscape of software engineering, developers frequently confront the challenge of translating conceptual ideas into functional code. while navigating this process, the gap between intention and implementation can often be a hurdle, even for experienced programmers. traditionally, developers have turned to online resources like stack overflow, searching for solutions in natural language to address their specific coding dilemmas. the emergence of large language models (llms) trained on code has heralded a new era in this domain. innovations like codex @xcite have revolutionized the field by providing real-time code suggestions in integrated development environments (ides). similarly, models such as @xcite demonstrate the potential for integrating into ides, offering developers context-aware assistance in initiating and refining code, thereby enhancing the efficiency of the software development cycle. however, the ascent of code generation through llms underscores the heightened need for datasets that emphasize precision, context-awareness, and syntactic accuracy. while existing datasets have propelled advancements in this arena, they have limitations. the shift towards llm-focused datasets has led to a decreased emphasis on traditional training sets, directing attention towards evaluation sets. this shift challenges the training of models from scratch or for specific task fine-tuning. moreover, while datasets like humaneval @xcite or apps @xcite provide valuable insights, they often fall short of mirroring the real-world coding challenges developers encounter. addressing these gaps, this paper introduces the codeinsight dataset, a resource specifically tailored for python code generation. this focus is anchored in python's widespread adoption in key sectors like data science, machine learning, and web development. the dataset, comprising 3,409 unique, expert-curated python examples, spans basic programming to complex data science challenges, complete with unit tests for evaluation. the codeinsight dataset stands out in its ability to provide a nuanced balance between breadth and depth, offering a finely-tuned resource for training and evaluating llms in python code generation. by bridging the gap between natural language and code, codeinsight presents a tool for understanding and enhancing the capabilities of llms in realworld programming contexts. the dataset provides three primary innovations, uniquely combined within this resource: organized as follows, this paper first details the dataset construction process in section 2, including our sources, selection criteria, and annotation methods. section 3 presents astatistical analysis of the dataset, highlighting its diverse applications. in section 4, the dataset's is tested through evaluations using llm baselines. lastly, section 5 situates codeinsight within the broader landscape of code generation datasets. ## dataset construction our pipeline for building codeinsight consists of three pivotal steps. initially, we identified the sources to retrieve examples. subsequently, from these sources, we filtered the most relevant natural language-code pairs. the final phase involved annotating these pairs and crafting associated unit tests. this section provides a breakdown of each of these stages. ## dataset statistics this section outlines the statistical framework of our dataset, highlighting the diversity of programming tasks and the complexity of the included code samples. we approach the analysis from two angles: the representation of code libraries and different labels representing the characteristics of code. key metrics such as item count, average words per natural language problem, and lines per code sample, alongside the depth of abstract syntax tree (ast) to complete code analysis, are presented to give keys of the difficulty of the dataset. ## baselines in this section, we test our dataset using state-ofthe-art llms for code generation. considering the volume and nature of our dataset, we explore various model evaluation methodologies. initially, we employ a zero-shot evaluation framework, augmenting it with pre-prompts to align better with our specialized task. subsequently, we experiment diverse partitioning strategies of the dataset for model fine-tuning, followed by evaluation on the remaining data. additionally, we conduct a comparative performance analysis of gpt-4 on a subset of conala examples and their modified versions by our annotators, to understand the impact of language and code remodeling relatively to data contamination. ## related works we introduce a comparative analysis, as detailed in featuring three distinct unit tests per example, a specialized training set, and predefined labels for in-depth performance analysis, the codeinsight dataset represents an unparalleled resource. it supports fine-tuning and a comprehensive evaluation of code generation models, offering a novel approach to enhance their development and assessment. ## conclusion in conclusion, codeinsight proposes a new framework for testing code generation, specialized in assisting developers. it adeptly links natural language and code in 3,409 problems, providing a robust platform for model training and evaluation. the dataset's strength lies in its diversity, expert annotation, and focus on practical coding scenarios, making it a valuable asset in the intersection of computational linguistics and code generation research. thanks to its categories, it allows a more precise comprehension of code generation model on this task and is completely compatible with other datasets for development aid.