Code completion, which is a crucial built-in support in many integrated development
environments (IDEs), is useful for developers during their daily programming activities.
Such a tool helps them to auto-complete detailed code during an editing session
and enable them to focus more on their high-level algorithmic solutions. Aiming to improve developers' productivity
in coding, a code completion tool
should be able to predict developers' intention and to automatically fill in
as much code as possible, while ensuring that the new code is consistent with the
current code under editing. However, most of code completion tools are limited to
filling in at the level of single method call or single object initialization. For
example, Eclipse's code completion provides an alphabetical-order list of available
methods for a variable under editing.
To predict user's intention for better auto-completion, several approaches
have been proposed to rank the recommended list of method calls. However, the volume
of auto-completed code is limited to a single method call for a variable. To address
that, other tools provide code templates for common programming constructs, e.g.
for/while loops with iterator objects. Unfortunately, as a template is filled in,
no context of currently edited code is considered..
GraPacc is designed with the goal of providing higher volume
of code yet correctly considering the context of the code under editing. It is a novel code completion tool
that is based on the frequent usages of APIs. When working with a library, developers
often repeat the correct ways of using APIs, which are called API usage patterns.
Each pattern is a valid combination of the usages of variables, method calls, control
structures and the orders among them. The patterns can be re-used in different tasks,
therefore, it will be helpful if a code completion tool can take advantage of usage
patterns to auto-complete more code for developers.
Based on that principle, GraPacc first uses GrouMiner to mine
and to build a database of frequent API usage patterns from the source code of any
given opensource projects. GraPacc also allows users to define and to upload their
own usage patterns.
an editing session, GraPacc extracts the context-sensitive features from the current
code, e.g. the API elements on focus and the current editing cursor, and their relations
to other code elements in order to rank the relevant API usage patterns and then
to auto-complete the current code with proper elements for the chosen pattern.
The evaluation results show that, GraPacc can
achieve up to 95% precision, 92% recall, and 93% f-score in code completion for
automatic accuracy measurement.
The result also
shows that, with GraPacc, human subjects were more productive because GraPacc can
help completing more code during programming. Their resulting code with GraPacc
had higher quality and was less buggy.