An Empirical Study of Software Exceptions in the Field using Search Logs

TL;DR

This study analyzes over a million web search queries related to software exceptions to understand how developers seek exception information online, using machine learning to extract exception data from large-scale query logs.

Contribution

It introduces a novel machine learning model for extracting exceptions from unstructured search queries and provides large-scale insights into exception search behavior across programming languages and domains.

Findings

Identified patterns in exception search queries and their popularity.

Analyzed effort and success rates in exception-related searches.

Provided insights for improving exception handling tools and documentation.

Abstract

Software engineers spend a substantial amount of time using Web search to accomplish software engineering tasks. Such search tasks include finding code snippets, API documentation, seeking help with debugging, etc. While debugging a bug or crash, one of the common practices of software engineers is to search for information about the associated error or exception traces on the internet. In this paper, we analyze query logs from a leading commercial general-purpose search engine (GPSE) such as Google, Yahoo! or Bing to carry out a large scale study of software exceptions. To the best of our knowledge, this is the first large scale study to analyze how Web search is used to find information about exceptions. We analyzed about 1 million exception related search queries from a random sample of 5 billion web search queries. To extract exceptions from unstructured query text, we built a novel and high-performance machine learning model with a F1-score of 0.82. Using the machine learning model, we extracted exceptions from raw queries and performed popularity, effort, success, query characteristic and web domain analysis. We also performed programming language-specific analysis to give a better view of the exception search behavior. These techniques can help improve existing methods, documentation and tools for exception analysis and prediction. Further, similar techniques can be applied for APIs, frameworks, etc.