Mining Likely Analogical APIs Across Third-Party Libraries via Large-Scale Unsupervised API Semantics Embedding

Chen, Chunyang; Xing, Zhenchang; Liu, Yang; Xiong, Kent Ong Long

doi:10.1109/tse.2019.2896123

Cited by 56 publications

(25 citation statements)

References 56 publications

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“…Therefore, SEQUER can effectively help users obtain better search results via high-quality query reformulation. Information Retrieval (IR) has been widely used in software engineering (SE) tasks, such as traceability recovery [56], [57], feature location [58], [59], library migration [2], [60], [61], API search [62], [63] and GUI design seeking [64]- [66]. In this section, we summarize the related works about query reformulation in general IR and its application in SE domain.…”

Section: E Discussionmentioning

confidence: 99%

Automated Query Reformulation for Efficient Search Based on Query Logs From Stack Overflow

Cao

Chen

Baltes

et al. 2021

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

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Section: E Discussionmentioning

confidence: 99%

Automated Query Reformulation for Efficient Search Based on Query Logs From Stack Overflow

Cao

Chen

Baltes

et al. 2021

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

Self Cite

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“…Moreover, we do not expect to have access to the declaration of those functions that are called by the query function. A very recent approach uses API embeddings (Nguyen et al 2017;Chen et al 2019), or joint natural text and API embeddings (Huang et al 2018) to find similar API usages. These techniques enable finding semantically equivalent APIs even if they do not share syntactical similarities.…”

Section: Api Selection and Usage Recommendationmentioning

confidence: 99%

Guided pattern mining for API misuse detection by change-based code analysis

et al. 2021

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Lack of experience, inadequate documentation, and sub-optimal API design frequently cause developers to make mistakes when re-using third-party implementations. Such API misuses can result in unintended behavior, performance losses, or software crashes. Therefore, current research aims to automatically detect such misuses by comparing the way a developer used an API to previously inferred patterns of the correct API usage. While research has made significant progress, these techniques have not yet been adopted in practice. In part, this is due to the lack of a process capable of seamlessly integrating with software development processes. Particularly, existing approaches do not consider how to collect relevant source code samples from which to infer patterns. In fact, an inadequate collection can cause API usage pattern miners to infer irrelevant patterns which leads to false alarms instead of finding true API misuses. In this paper, we target this problem (a) by providing a method that increases the likelihood of finding relevant and true-positive patterns concerning a given set of code changes and agnostic to a concrete static, intra-procedural mining technique and (b) by introducing a concept for just-in-time API misuse detection which analyzes changes at the time of commit. Particularly, we introduce different, lightweight code search and filtering strategies and evaluate them on two real-world API misuse datasets to determine their usefulness in finding relevant intra-procedural API usage patterns. Our main results are (1) commit-based search with subsequent filtering effectively decreases the amount of code to be analyzed, (2) in particular method-level filtering is superior to file-level filtering, (3) project-internal and project-external code search find solutions for different types of misuses and thus are complementary, (4) incorporating prior knowledge of the misused API into the search has a negligible effect.

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“…Although the embedding-based approach taken by API2Vec [32] is flexible, the resulting ambiguity is in fact a hindrance when performing migrations. More recent work attempts to generate mappings between APIs based on their usage [7].…”

Section: Related Work 81 Library Migrationmentioning

confidence: 99%

M ³

Collie

Ginsbach²,

Woodruff

et al. 2020

Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering

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Library migration is a challenging problem, where most existing approaches rely on prior knowledge. This can be, for example, information derived from changelogs or statistical models of API usage.This paper addresses a different API migration scenario where there is no prior knowledge of the target library. We have no historical changelogs and no access to its internal representation. To tackle this problem, this paper proposes a novel approach (M 3 ), where probabilistic program synthesis is used to semantically model the behavior of library functions. Then, we use an SMT-based code search engine to discover similar code in user applications. These discovered instances provide potential locations for API migrations.We evaluate our approach against 7 well-known libraries from varied application domains, learning correct implementations for 94 functions. Our approach is integrated with standard compiler tooling, and we use this integration to evaluate migration opportunities in 9 existing C/C++ applications with over 1MLoC. We discover over 7,000 instances of these functions, of which more than 2,000 represent migration opportunities. CCS CONCEPTS• Software and its engineering → Software maintenance tools; Software verification and validation; Compilers.

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Mining Likely Analogical APIs Across Third-Party Libraries via Large-Scale Unsupervised API Semantics Embedding

Cited by 56 publications

References 56 publications

Automated Query Reformulation for Efficient Search Based on Query Logs From Stack Overflow

Automated Query Reformulation for Efficient Search Based on Query Logs From Stack Overflow

Guided pattern mining for API misuse detection by change-based code analysis

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