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Register a new userAnalyzing Web Search Behavior for Software Engineering Tasks
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Added by
Chetan Bansal
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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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 the search query logs from Bing, a commercial web search engine. First, we use distant supervision techniques to build a machine learning classifier to extract the SE search queries with an F1 score of 93%. We then perform an analysis on one million search sessions to understand how software engineering related queries and sessions differ from other queries and sessions. Subsequently, we propose a taxonomy of intents to identify the various contexts in which web search is used in software engineering. Lastly, we analyze millions of SE queries to understand the distribution, search metrics and trends across these SE search intents. Our analysis shows that SE related queries form a significant portion of the overall web search traffic. Additionally, we found that there are six major intent categories for which web search is used in software engineering. The techniques and insights can not only help improve existing tools but can also inspire the development of new tools that aid in finding information for SE related tasks.
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Nowadays, invoking third party code increasingly involves calling web services via their web APIs, as opposed to the more traditional scenario of downloading a library and invoking the librarys API. However, there are also new challenges for developers calling these web APIs. In this paper, we highlight a broad set of these challenges and argue for resulting opportunities for software engineering research to support developers in consuming web APIs. We outline two specific research threads in this context: (1) web API specification curation, which enables us to know the signatures of web APIs, and (2) static analysis that is capable of extracting URLs, HTTP methods etc. of web API calls. Furthermore, we present new work on how we combine (1) and (2) to provide IDE support for application developers consuming web APIs. As web APIs are used broadly, research in supporting the consumption of web APIs offers exciting opportunities.
Empirical Standards are natural-language models of a scientific communitys expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.
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.
In this paper we introduce the notion of Modal Software Engineering: automatically turning sequential, deterministic programs into semantically equivalent programs efficiently operating on inputs coming from multiple overlapping worlds. We are drawing an analogy between modal logics, and software application domains where multiple sets of inputs (multiple worlds) need to be processed efficiently. Typically those sets highly overlap, so processing them independently would involve a lot of redundancy, resulting in lower performance, and in many cases intractability. Three application domains are presented: reasoning about feature-based variability of Software Product Lines (SPLs), probabilistic programming, and approximate programming.
The main problems of Software Engineering appear as a result of incompatibilities. For example, the quality of organization of the production process depends on correspondence with existent resources and on a common understanding of project goals by all team members. Software design is another example. Its successfulness rides on the architectures conformity with a projects concepts. This is a point of great nicety. All elements should create a single space of interaction. And if the laws of such a space are imperfect, missequencing comes and the concept of a software system fails. We must do our best for this not to happen. To that end, having a subtle perception of systems structures is essential. Such knowledge can be based only on a fresh approach to the logical law.
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