Analyzing Web Search Behavior for Software Engineering Tasks

Rao, Nikitha; Bansal, Chetan; Zimmermann, Thomas; Awadallah, Ahmed Hassan; Nagappan, Nachiappan

doi:10.1109/bigdata50022.2020.9378083

Cited by 10 publications

(6 citation statements)

References 33 publications

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“…Logs of actual Web search queries are usually not made publicly available by search engines. Rao et al note that a significant bottleneck in this area of research is the lack of datasets as search logs can not be made public due to privacy laws (see also Section 5) [4]. In their later work [17], they release a limited dataset of anonymized software development-related search queries mined from Bing logs between September 1, 2019 and August 31, 2020.…”

Section: Software Development-related Queriesmentioning

confidence: 99%

“…However, numerous developer searches are unsuccessful, consuming valuable developer time and effort. In a recent large-scale study of one million Web search sessions by software developers using Bing, Rao et al found that software engineering-related queries are less effective than other types of queries [4], resulting in higher rates of query reformulations, fewer clicks, and shorter dwell time compared to non software engineering sessions.…”

Section: Introductionmentioning

confidence: 99%

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Using clarification questions to improve software developers' Web search

Imran,

Damevski

2022

Preprint

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ContextRecent research indicates that Web queries written by software developers are not very successful in retrieving relevant results, performing measurably worse compared to general purpose Web queries. Most approaches up to this point have addressed this problem with software engineering-specific automated query reformulation techniques, which work without developer involvement but are limited by the content of the original query. In other words, these techniques automatically improve the existing query but can not contribute new, previously unmentioned, concepts.

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Section: Software Development-related Queriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using clarification questions to improve software developers' Web search

Imran,

Damevski

2022

Preprint

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“…Wang et al have leveraged intent understanding for improving effort estimation in code reviews [17], [18]. Recently, software engineering related search queries have been analyzed and classified into different categories by using distant supervision [7] and tokenlevel intent aggregation [8]. Our goal is to further improve upon these methods by introducing a weak supervision based approach for code search intent classification.…”

Section: Background and Motivationmentioning

confidence: 99%

“…In the context of code search intent classification, we leverage the software engineering sub-intent classifiers (such as Debug, HowTo, etc.) proposed by Rao et al [7]. We also introduce learning functions to identify patterns which indicate code examples, error codes and exceptions.…”

Section: A Generative Model Pipelinementioning

confidence: 99%

“…In order to create a large-scale dataset of code search queries, it is crucial to automatically detect code search intent in search queries. Previous research in the area of search query classification [7], [8] has focused primarily on classification of web queries in categories such as Debug, API, and HowTo using heuristics and rule-based methods which tend to overfit.…”

Section: Introductionmentioning

confidence: 99%

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Search4Code: Code Search Intent Classification Using Weak Supervision

Rao¹,

Bansal²,

Guan³

2020

Preprint

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Developers use search for various tasks such as finding code, documentation, debugging information, etc. In particular, web search is heavily used by developers for finding code examples and snippets during the coding process. Recently, natural language based code search has been an active area of research. However, the lack of real-world large-scale datasets is a significant bottleneck. In this work, we propose a weak supervision based approach for detecting code search intent in search queries for C# and Java programming languages. We evaluate the approach against several baselines on a real-world dataset comprised of over 1 million queries mined from Bing web search engine and show that the CNN based model can achieve an accuracy of 77% and 76% for C# and Java respectively. Furthermore, we are also releasing the first large-scale real-world dataset of code search queries mined from Bing web search engine. We hope that the dataset will aid future research on code search.

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