Mining Readme Files to Support Automatic Building of Java Projects in Software Repositories

Hassan, Foyzul; Wang, Xiaoyin

doi:10.1109/icse-c.2017.114

Cited by 19 publications

(4 citation statements)

References 2 publications

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“…Abebe et al [57] study contained information in release notes of software to give developers empirically-supported suggestions in the writing of these notes. Hassan & Wang [58] use README files as a source for automatic extraction of software build commands. Zhang et al [59] propose a method to detect similar repositories on GitHub based on similar contents of the README files.…”

Section: Study Of Software Documentsmentioning

confidence: 99%

Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers

Lin,

Wang,

et al. 2022

Preprint

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Source code is essential for researchers to reproduce the methods and replicate the results of artificial intelligence (AI) papers. Some organizations and researchers manually collect AI papers with available source code to contribute to the AI community. However, manual collection is a labor-intensive and time-consuming task. To address this issue, we propose a method to automatically identify papers with available source code and extract their source code repository URLs. With this method, we find that 20.5% of regular papers of 10 top AI conferences published from 2010 to 2019 are identified as papers with available source code and that 8.1% of these source code repositories are no longer accessible. We also create the XMU NLP Lab README Dataset, the largest dataset of labeled README files for source code document research. Through this dataset, we have discovered that quite a few README files have no installation instructions or usage tutorials provided. Further, a large-scale comprehensive statistical analysis is made for a general picture of the source code of AI conference papers. The proposed solution can also go beyond AI conference papers to analyze other scientific papers from both journals and conferences to shed light on more domains.

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Section: Study Of Software Documentsmentioning

confidence: 99%

Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers

Lin,

Wang,

et al. 2022

Preprint

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“…Sharma et al (2017) combine automated topic extraction with manual validation to categorise GitHub repositories based on the content of README files. Furthermore, Hassan and Wang (2017) propose the use of both qualitative and quantitative approaches to automatically detect instructions for software development in project description files.…”

Section: Content Analysis Of Open Source Projectsmentioning

confidence: 99%

Learning to Classify Morals and Conventions: Artificial Intelligence in Terms of the Economics of Convention

Solans

Tauchmann

Farrell

et al. 2021

ICWSM

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Artificial Intelligence (AI) and its relation with societies has become an increasingly interesting subject of study for the social sciences. Nevertheless, there is still an important lack of interdisciplinary and empirical research applying social theories to the field of AI. We here aim to shed light on the interactions between humans and autonomous systems and analyse the moral conventions, which underly these interactions and cause moments of conflict and cooperation. For this purpose we employ the Economics of Convention (EC), originally developed in the context of economic processes of production and management involving humans, objects and machines. We create a dataset from three relevant text sources and perform a qualitative exploration of its content. Then, we train a combination of Machine Learning (ML) classifiers on this dataset, which achieve an average classification accuracy of 83.7%. A qualitative and quantitative evaluation of the predicted conventions reveals, inter alia, that the Industrial and Inspired conventions tend to co-exist in the AI domain. This is the first time, ML classifiers are used to study the EC in different AI-related text types. Our analysis of a larger dataset is especially beneficial for the social sciences.

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“…Named Entity Recognition(NER) is a Natural Language Processing(NLP) technique to identify entities from text and classify them into the defined categories. NER is widely used in different languages processing applications, such as newspaper content classification [46], Q&A systems, and machine translation [25], extract software project artifact information from document [27]. NER solutions can be divided into two broad categories: i) rulebased and ii) statistical pattern-based.…”

Section: Model For Extracting Exceptionsmentioning

confidence: 99%

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

Hassan¹,

Bansal²,

Nagappan³

et al. 2020

Preprint

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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 highperformance 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.

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Mining Readme Files to Support Automatic Building of Java Projects in Software Repositories

Cited by 19 publications

References 2 publications

Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers

Automatic Analysis of Available Source Code of Top Artificial Intelligence Conference Papers

Learning to Classify Morals and Conventions: Artificial Intelligence in Terms of the Economics of Convention

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

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