Results of the summarizing process for the arXiv paper: 2212.07253v1

API-Spector is a recommendation engine that assists developers in designing sustainable APIs by retrieving relevant OpenAPI specification components from a public or internal database. It addresses the lack of standardized methods for comparing API designs by using innovative feature extraction techniques optimized for the highly technical API specification domain. These include novel methods of processing and extracting key information from OpenAPI specifications as well as natural language text similarity to characterize endpoints based on descriptions and summaries found in the operations section. To obtain contextual representations of the text, pre-trained language models such as BERT and SENT-BERT are used. Additionally, keyword tokens are obtained through pre-processing the entire text which generates TF-IDF and PPMI scores for these keywords allowing representation of domain-specific terms not well represented in pre-trained language models. The enriched text features obtained by combining both approaches consistently outperform other text features across all evaluation tasks. User studies evaluating API-Spector's usefulness in a realistic scenario achieved best performance with 0.690 recall, 0.475 precision, and 0.562 F1 scores compared to baseline models. Overall, API-Spector provides guidance to accelerate development processes thoughtfully designed APIs that promote code maintainability and quality while allowing developers to learn from existing established examples and potentially identify redundancies in their work.

• API-Spector is a recommendation engine that helps developers design sustainable APIs
• It retrieves relevant OpenAPI specification components from public or internal databases
• It addresses the lack of standardized methods for comparing API designs
• Innovative feature extraction techniques are used, optimized for the highly technical API specification domain
• Novel methods of processing and extracting key information from OpenAPI specifications are used
• Natural language text similarity is used to characterize endpoints based on descriptions and summaries found in the operations section
• Pre-trained language models such as BERT and SENT-BERT are used to obtain contextual representations of the text
• Keyword tokens are obtained through pre-processing the entire text which generates TF-IDF and PPMI scores for these keywords allowing representation of domain-specific terms not well represented in pre-trained language models.
• The enriched text features obtained by combining both approaches consistently outperform other text features across all evaluation tasks.
• User studies evaluating API-Spector's usefulness achieved best performance with 0.690 recall, 0.475 precision, and 0.562 F1 scores compared to baseline models.
• Overall, API-Spector provides guidance to accelerate development processes thoughtfully designed APIs that promote code maintainability and quality while allowing developers to learn from existing established examples and potentially identify redundancies in their work.

Introducing API-Spector: A Recommendation Engine for Designing Sustainable APIs

API development is a complex process that requires developers to have an in-depth understanding of the technology and tools used. As such, it can be difficult to create APIs that are both efficient and maintainable. To help address this challenge, researchers from the University of California, Santa Cruz have developed API-Spector – a recommendation engine designed to assist developers in designing sustainable APIs.

What is API-Spector?

API-Spector is a tool that helps developers design better APIs by retrieving relevant OpenAPI specification components from either a public or internal database. It addresses the lack of standardized methods for comparing API designs by using innovative feature extraction techniques optimized for the highly technical domain of API specifications. These include novel methods of processing and extracting key information from OpenAPI specifications as well as natural language text similarity to characterize endpoints based on descriptions and summaries found in the operations section.

How Does It Work?

To obtain contextual representations of the text, pre-trained language models such as BERT and SENT-BERT are used. Additionally, keyword tokens are obtained through pre-processing the entire text which generates TF-IDF (term frequency–inverse document frequency) and PPMI (pointwise mutual information) scores for these keywords allowing representation of domain specific terms not well represented in pre-trained language models. The enriched text features obtained by combining both approaches consistently outperform other text features across all evaluation tasks.

Evaluating Performance

User studies evaluating API Spector's usefulness in a realistic scenario achieved best performance with 0.690 recall, 0.475 precision, and 0.562 F1 scores compared to baseline models . Overall, results indicate that API Spector provides guidance to accelerate development processes thoughtfully designed APIs that promote code maintainability and quality while allowing developers to learn from existing established examples and potentially identify redundancies in their work .

Conclusion

The research paper presented here demonstrates how API Spector can provide valuable assistance when developing sustainable APIs through its innovative feature extraction techniques optimized specifically for this domain . By leveraging pre - trained language models , keyword tokens , TF - IDF , PPMI scores , etc . , it allows users to quickly compare different designs while learning from existing examples . User studies show promising results with respect to recall , precision , F1 score metrics compared against baseline models .