In the field of automated program repair, the redundancy assumption claims
large programs contain the seeds of their own repair. However, most
redundancy-based program repair techniques do not reason about the repair
ingredients---the code that is reused to craft a patch. We aim to reason about
the repair ingredients by using code similarities to prioritize and transform
statements in a codebase for patch generation. Our approach, DeepRepair, relies
on deep learning to reason about code similarities. Code fragments at
well-defined levels of granularity in a codebase can be sorted according to
their similarity to suspicious elements (i.e., code elements that contain
suspicious statements) and statements can be transformed by mapping
out-of-scope identifiers to similar identifiers in scope. We examined these new
search strategies for patch generation with respect to effectiveness from the
viewpoint of a software maintainer. Our comparative experiments were executed
on six open-source Java projects including 374 buggy program revisions and
consisted of 19,949 trials spanning 2,616 days of computation time.
DeepRepair's search strategy using code similarities generally found compilable
ingredients faster than the baseline, jGenProg, but this improvement neither
yielded test-adequate patches in fewer attempts (on average) nor found
significantly more patches than the baseline. Although the patch counts were
not statistically different, there were notable differences between the nature
of DeepRepair patches and baseline patches. The results demonstrate that our
learning-based approach finds patches that cannot be found by existing
redundancy-based repair techniques.

利用深度学习技术推断代码相似度来优先排序、转化代码库中的语句以生成补丁。DeepRepair 能够快速生成可编译的补丁，然而不比现有的缺陷修复技术更快地生成测试足够的补丁，但结果表明，该方法能够找到现有的基于冗余假设缺陷修复方法无法发现的补丁。

深度学习代码相似性用于程序修复成分的排序和转换

Sorting and Transforming Program Repair Ingredients via Deep Learning  Code Similarities

This paper presents a new technique for automatically synthesizing SQL
queries from natural language. Our technique is fully automated, works for any
database without requiring additional customization, and does not require users
to know the underlying database schema. Our method achieves these goals by
combining natural language processing, program synthesis, and automated program
repair. Given the user's English description, our technique first uses semantic
parsing to generate a query sketch, which is subsequently completed using
type-directed program synthesis and assigned a confidence score using database
contents. However, since the user's description may not accurately reflect the
actual database schema, our approach also performs fault localization and
repairs the erroneous part of the sketch. This synthesize-repair loop is
repeated until the algorithm infers a query with a sufficiently high confidence
score. We have implemented the proposed technique in a tool called Sqlizer and
evaluate it on three different databases. Our experiments show that the desired
query is ranked within the top 5 candidates in close to 90% of the cases.

本文提出了一种新技术，可以从自然语言中自动合成 SQL 查询，并采用自然语言处理，程序合成和自动程序修复相结合的方法来增强其鲁棒性。