Transfer Learning for Cross-Platform Software Crowdsourcing Recommendation

Yan, Shuhan; Shen, Beijun; Mo, Wenkai; Li, Ning

doi:10.1109/apsec.2017.33

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…Domain adaptation methods are useful for other software engineering tasks that involve two different domains targeted by transferred learning [19,20,36], such as cross-language program classification, cross-language/project bug prediction. These tasks may benefit from the proposed approach when little curated data is available.…”

Section: Discussionmentioning

confidence: 99%

SAR: learning cross-language API mappings with little knowledge

Bui

Jiang

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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Section: Discussionmentioning

confidence: 99%

SAR: learning cross-language API mappings with little knowledge

Bui

Jiang

2019

Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of

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“…Domain adaptation methods are useful for other software engineering tasks that involve two different domains targeted by transferred learning [17,18,33], such as cross-language program classification, code summarization, cross-language/project bug prediction. These tasks may benefit from the proposed approach when little curated data is available.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical learning of cross-language mappings through distributed vector representations for code

Bui

Jiang

2018

Proceedings of the 40th International Conference on Software Engineering: New Ideas and Emerging Results

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To save effort, developers often translate programs from one programming language to another, instead of implementing it from scratch. Translating application program interfaces (APIs) used in one language to functionally equivalent ones available in another language is an important aspect of program translation. Existing approaches facilitate the translation by automatically identifying the API mappings across programming languages. However, these approaches still require large amount of parallel corpora, ranging from pairs of APIs or code fragments that are functionally equivalent, to similar code comments.To minimize the need of parallel corpora, this paper aims at an automated approach that can map APIs across languages with much less a priori knowledge than other approaches. The approach is based on an realization of the notion of domain adaption, combined with code embedding, to better align two vector spaces. Taking as input large sets of programs, our approach first generates numeric vector representations of the programs (including the APIs used in each language), and it adapts generative adversarial networks (GAN) to align the vectors in different spaces of two languages. For a better alignment, we initialize the GAN with parameters derived from API mapping seeds that can be identified accurately with a simple automatic signature-based matching heuristic. Then the crosslanguage API mappings can be identified via nearest-neighbors queries in the aligned vector spaces. We have implemented the approach (SAR, named after three main technical components in the approach) in a prototype for mapping APIs across Java and C# programs. Our evaluation on about 2 million Java files and 1 million C# files shows that the approach can achieve 54% and 82% mapping accuracy in its top-1 and top-10 API mapping results with only 257 automatically identified seeds, more accurate than other approaches using the same or much more mapping seeds.

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“…In Abel et al ’s (2011) study, they utilized the rich text information in the news domain to enrich the user semantic representation on Twitter and in turn boosted the performance of personalized news recommendation. Yan et al (2017) proposed a novel cross-platform recommendation method for new software crowdsourcing platforms, the idea of which was to transfer data and knowledge from other mature software crowdsourcing platforms (source domains) to solve the insufficient recommendation model training problem on a new platform (target domain). In Wu et al ’s (2018) study, they formulated the cross-platform video recommendation problem as follows: given a set of aligned users U, for each user u ∈ U, his or her behaviours on the social platform are observed (i.e.…”

Section: Related Workmentioning

confidence: 99%

A cross-platform recommendation system from Facebook to Instagram

Chang

Chen

2023

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Purpose The purpose of this paper is to provide a cross-platform recommendation system that recommends the most suitable public Instagram accounts to Facebook users. Design/methodology/approach We collect data from both Facebook and Instagram and then propose a similarity matching mechanism for recommending the most appropriate Instagram accounts to Facebook users. By removing the data disparity between the two heterogeneous platforms and integrating them, the system is able to make more accurate recommendations. Findings The results show that the method proposed in this paper can recommend suitable public Instagram accounts to Facebook users with very high accuracy. Originality/value To the best of the authors’ knowledge, this is the first study to propose a recommender system to recommend Instagram public accounts to Facebook users. Second, our proposed method can integrate heterogeneous data from two different platforms to generate collaborative recommendations. Furthermore, our cross-platform system reveals an innovative concept of how multiple platforms can promote their respective platforms in a unified, cooperative and collaborative manner.

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Transfer Learning for Cross-Platform Software Crowdsourcing Recommendation

Cited by 7 publications

References 25 publications

SAR: learning cross-language API mappings with little knowledge

SAR: learning cross-language API mappings with little knowledge

Hierarchical learning of cross-language mappings through distributed vector representations for code

A cross-platform recommendation system from Facebook to Instagram

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