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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.
Being light-weight and cost-effective, IR-based approaches for bug localization have shown promise in finding software bugs. However, the accuracy of these approaches heavily depends on their used bug reports. A significant number of bug reports cont
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Cyber attacks are increasingly becoming prevalent and causing significant damage to individuals, businesses and even countries. In particular, ransomware attacks have grown significantly over the last decade. We do the first study on mining insights
Web search plays an integral role in software engineering (SE) to help with various tasks such as finding documentation, debugging, installation, etc. In this work, we present the first large-scale analysis of web search behavior for SE tasks using t
Software engineering bots - automated tools that handle tedious tasks - are increasingly used by industrial and open source projects to improve developer productivity. Current research in this area is held back by a lack of consensus of what software