Stepwise API usage assistance based on N-gram language models

• Autor(a) principal: Prendi, Gonçalo Queiroga
• Data de Publicação: 2015
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10071/10910
• topic: N-gram language models; API usability; Perplexity; Code completion; Usabilidade das APIs; Perplexidade

Software development requires the use of external Application Programming Interfaces (APIs) in order to reuse libraries and frameworks. Programmers often struggle with unfamiliar APIs due to their lack of resources or less common design. Such difficulties often lead to an incorrect sequences of API calls that may not produce the desired outcome. Language models have shown the ability to capture regularities in text as well as in code. In this work we explore the use of n-gram language models and their ability to capture regularities in API usage through an intrinsic and extrinsic evaluation of these models on some of the most widely used APIs for the Java programming language. To achieve this, several language models were trained over a source code corpora containing several hundreds of GitHub Java projects that use the desired APIs. In order to fully assess the performance of the language models, we have selected APIs from multiple domains and vocabulary sizes. This work allowed us to conclude that n-gram language models are able to capture the API usage patterns due to their low perplexity values and their high overall coverage, going up to 100% in some cases, which encouraged us to create a code completion tool to help programmers stay in the right path when using unknown APIs while allowing for some exploration.