Import2vec: Learning Embeddings for Software Libraries

Theeten, Bart; Vandeputte, Frederik; Cutsem, Tom Van

doi:10.1109/msr.2019.00014

Cited by 27 publications

(14 citation statements)

References 17 publications

(24 reference statements)

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“…The more recent Import2Vec [37] paper produces em-beddings for each imported package. The authors do such embeddings for JavaScript, Python, and Java, and provide some qualitative evidence suggesting that these embeddings of APIs accurately reflect different functionality profiles by providing a number of examples where the similar APIs also appear to implement similar functionalities.…”

Section: A Developer Expertisementioning

confidence: 99%

Representation of Developer Expertise in Open Source Software

Dey

Karnauch

Mockus

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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Background: Accurate representation of developer expertise has always been an important research problem. While a number of studies proposed novel methods of representing expertise within individual projects, these methods are difficult to apply at an ecosystem level. However, with the focus of software development shifting from monolithic to modular, a method of representing developers' expertise in the context of the entire OSS development becomes necessary when, for example, a project tries to find new maintainers and look for developers with relevant skills. Aim: We aim to address this knowledge gap by proposing and constructing the Skill Space where each API, developer, and project is represented and postulate how the topology of this space should reflect what developers know (and projects need). Method: we use the World of Code infrastructure to extract the complete set of APIs in the files changed by open source developers and, based on that data, employ Doc2Vec embeddings for vector representations of APIs, developers, and projects. We then evaluate if these embeddings reflect the postulated topology of the Skill Space by predicting what new APIs/projects developers use/join, and whether or not their pull requests get accepted. We also check how the developers' representations in the Skill Space align with their self-reported API expertise. Result: Our results suggest that the proposed embeddings in the Skill Space appear to satisfy the postulated topology and we hope that such representations may aid in the construction of signals that increase trust (and efficiency) of open source ecosystems at large and may aid investigations of other phenomena related to developer proficiency and learning.

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Section: A Developer Expertisementioning

confidence: 99%

Representation of Developer Expertise in Open Source Software

Dey

Karnauch

Mockus

2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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“…There are many studies on the representation of source code, including recent studies proposing distributed representations for identifiers [17], APIs [46,47], and software libraries [56]. A comprehensive survey of learning the representation of source code has been done by Allamanis et al [1].…”

Section: Related Workmentioning

confidence: 99%

CC2Vec

Hoang

Kang

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

127

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Existing work on software patches often use features specific to a single task. These works often rely on manually identified features, and human effort is required to identify these features for each task. In this work, we propose CC2Vec, a neural network model that learns a representation of code changes guided by their accompanying log messages, which represent the semantic intent of the code changes. CC2Vec models the hierarchical structure of a code change with the help of the attention mechanism and uses multiple comparison functions to identify the differences between the removed and added code. To evaluate if CC2Vec can produce a distributed representation of code changes that is general and useful for multiple tasks on software patches, we use the vectors produced by CC2Vec for three tasks: log message generation, bug fixing patch identification, and just-in-time defect prediction. In all tasks, the models using CC2Vec outperform the state-of-the-art techniques.

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“…Dependency similarity. As a proxy for functional similarity between two repositories (H 5 ), we compute the degree to which their sets of dependencies are similar [70]. Specifically, we consider each set of dependencies as a "document" and compute the TF-IDF correlation matrix similarity score of the two documents [10].…”

Section: Other Operationalizationsmentioning

confidence: 99%