FACER: An API usage-based code-example recommender for opportunistic reuse

Abid, Shamsa; Shamail, Shafay; Basit, Hamid Abdul; Nadi, Sarah

doi:10.1007/s10664-021-10000-w

Cited by 9 publications

(3 citation statements)

References 84 publications

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“…In our study, we address the construction of a system that accumulates and searches for a broader range of software development knowledge. Abid et al proposed a system for recommending code snippets tailored to specific development objectives, confirming that these recommendations can facilitate code reuse [6]. Their research targeted only the passive recommendation of source code snippets.…”

Section: Related Researchmentioning

confidence: 98%

“…In this proposal, search queries are determined by matching keywords with words in the cases. To improve this, it would be possible to integrate more advanced research techniques such as fuzzy search in order to hit more candidates or recommendations based on development objectives described in natural language, as has been explored in code snippet search research [6,51]. For displaying the results, we propose a system that sorts by user-selected criteria such as registration date and highlights matching words.…”

Section: Advantages and Limitationsmentioning

confidence: 99%

“…Today, software reuse is realized and made available in various forms, such as software libraries, design patterns, and software frameworks, embodying typical software functionalities, practices, and architectures [2][3][4]. Moreover, research is advancing on the partial and small-scale reuse of existing software in other software developments through the reuse of code snippets [5,6]. Building on these achievements, as surveyed by Barros-Justo and others, modern software practices are increasingly leveraging reuse in various forms based on past assets [7].…”

Section: Introductionmentioning

confidence: 99%

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SUCCEED: Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development

Nakata,

Chen,

Saiki

et al. 2023

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Software upcycling, a form of software reuse, is a concept that efficiently generates novel, innovative, and value-added development projects by utilizing knowledge extracted from past projects. However, how to integrate the materials derived from these projects for upcycling remains uncertain. This study defines a systematic model for upcycling cases and develops the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system to support the implementation of new upcycling initiatives by effectively sharing cases within the organization. To ascertain the efficacy of upcycling within our proposed model and system, we formulated three research questions and conducted two distinct experiments. Through surveys, we identified motivations and characteristics of shared upcycling-relevant development cases. Development tasks were divided into groups, those that employed the SUCCEED system and those that did not, in order to discern the enhancements brought about by upcycling. As a result of this research, we accomplished a comprehensive structuring of both technical and experiential knowledge beneficial for development, a feat previously unrealizable through conventional software reuse, and successfully realized reuse in a proactive and closed environment through construction of the wisdom of crowds for upcycling cases. Consequently, it becomes possible to systematically perform software upcycling by leveraging knowledge from existing projects for streamlining of software development.

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Section: Related Researchmentioning

confidence: 98%

Section: Advantages and Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SUCCEED: Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development

Nakata,

Chen,

Saiki

et al. 2023

Information

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Engineering recommender systems for modelling languages: concept, tool and evaluation

Almonte,

Guerra,

Cantador

et al. 2024

Empir Software Eng

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Recommender systems (RSs) are ubiquitous in all sorts of online applications, in areas like shopping, media broadcasting, travel and tourism, among many others. They are also common to help in software engineering tasks, including software modelling, where we are recently witnessing proposals to enrich modelling languages and environments with RSs. Modelling recommenders assist users in building models by suggesting items based on previous solutions to similar problems in the same domain. However, building a RS for a modelling language requires considerable effort and specialised knowledge. To alleviate this problem, we propose an automated, model-driven approach to create RSs for modelling languages. The approach provides a domain-specific language called Droid to configure every aspect of the RS: the type of the recommended modelling elements, the gathering and preprocessing of training data, the recommendation method, and the metrics used to evaluate the created RS. The RS so configured can be deployed as a service, and we offer out-of-the-box integration with Eclipse modelling editors. Moreover, the language is extensible with new data sources and recommendation methods. To assess the usefulness of our proposal, we report on two evaluations. The first one is an offline experiment measuring the precision, completeness and diversity of recommendations generated by several methods. The second is a user study – with 40 participants – to assess the perceived quality of the recommendations. The study also contributes with a novel evaluation methodology and metrics for RSs in model-driven engineering.

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