AutoQuery: automatic construction of dependency queries for code search

Abstract

Many code search techniques have been proposed to return relevant code for a user query expressed as textual descriptions. However, source code is not mere text. It contains dependency relations among various program elements. To leverage these dependencies for more accurate code search results, techniques have been proposed to allow user queries to be expressed as control and data dependency relationships among program elements. Although such techniques have been shown to be effective for finding relevant code, it remains a question whether appropriate queries can be generated by average users. In this work, we address this concern by proposing a technique, AutoQuery, that can automatically construct dependency queries from a set of code snippets. We realize AutoQuery by the following major steps: firstly, code snippets (that are not necessarily compilable) are converted into program dependence graphs (PDGs); secondly, a new graph mining solution is built to return common structures in the PDGs; thirdly, the common structures are converted to dependency queries, which are used to retrieve results by using a dependence-based code search technique. We have evaluated AutoQuery on real systems with 47 different code search tasks. The results show that the automatically constructed dependency queries retrieve relevant code with a precision, recall, and F-measure of 68.4, 72.1, and 70.2 %, respectively. We have also performed a user study to compare the effectiveness of AutoQuery with that of human generated queries. The results show that queries constructed by AutoQuery on average help to retrieve code fragments with comparable F-measures to those retrieved by human constructed queries.

User Interface Code Retrieval: A Novel Visual-Representation-Aware Approach

“…This is natural because the boundary between these intervals is indeed more blurred, and it is thus more difficult to give an accurate rating. It is also interesting to observe that the MSE in interval [4,5] is much smaller. This is probably because these pairs are indeed highly similar, allowing our method to generate very accurate ratings.…”

“…We plot the final rating distribution of all interface pairs in Figure 5. The ratings are discretized into four intervals: [1,2), [2,3), [3,4), and [4,5]. It can be observed that this test set contains sufficient similar interface pairs to validate our algorithm.…”

Recommending source code locations for system specific transformations

“…Then, it searches for code with dependencies in common, such as the methods being called, to find where small-scale changes should be applied. Another tool that leverages the dependency relations among various program elements is AUTOQUERY [31]. It relies on conversion of code snippets into program dependence graphs and searches using dependence-based code search technique.…”