Existing studies show that code summaries help developers understand and maintain source code. Unfortunately, these summaries are often mismatched, missing or outdated in software projects. Code summarization aims to generate brief and accurate natural language descriptions automatically for source code. According to Gros et al., code summaries are highly structured and have many repetitive patterns, for example, they are often begin with patterns like “return true if…” and “create a new…”. The promising results obtained by previous approaches also prove the existence of these patternized words. Besides the patternized words, a code summary also contains important keywords, which are the key to reflecting the functionality of the code. However, the state-of-the-art code summarization approaches perform poorly on predicting the keywords, which leads to the generated summaries suffer a loss in informativeness. To alleviate this problem, this paper proposes a novel retrieve-and-edit approach named EditSum for code summarization. Specifically, EditSum first retrieves a similar code snippet from a pre-defined corpus and treats its summary as a prototype summary to learn the pattern. Then, EditSum edits the prototype automatically to combine the pattern in the prototype with the semantic information of input code. Our motivation is that the retrieved prototype provides a good start-point for post-generation because the summaries of similar code snippets often have the same pattern. The post-editing process further reuses the patternized words in prototype and generates keywords based on the semantic information of code. We conduct experiments on a large-scale Java corpus, which contains about 2M samples, and experimental results demonstrate that EditSum outperforms the state-of-the-art approaches by a substantial margin. The human evaluation also proves the summaries generated by EditSum are more informative and useful. We also verify that EditSum performs well on predicting the patternized words and keywords. The code and data will be open-sourced.

Jia Allen Li

Peking University

Yongmin Li

Peking University

Ge Li

Peking University

Xing Hu

Zhejiang University

Xin Xia

Huawei Software Engineering Application Technology Lab

China

Zhi Jin

Peking University

China

Program translation is necessary in many real-world scenarios, such as porting codebases from an obsolete or deprecated language to a modern one or re-implementing existing projects in one’s preferred programming language. One way to automate program translation is to make use of Big Code. Existing data-driven approaches either training a translation model or leveraging cross-language retrieval. The former requires large amounts of training data and extra information or neglects significant characteristics of programs. The latter has a barrier to finding the translation with only the features of the input program as the query. In this paper, we present BigPT for interactive cross-language retrieval from Big Code only based on raw code and reusing the retrieved code to assist program translation. We build on existing work on cross-language code representation and we propose a novel predictive transformation model based on auto-encoders. The model is trained on Big Code to generate a target-language representation, which will be used as the query to retrieve the most relevant translations for a given program. Our succinct query enables the user to easily update and correct the returned results to improve the retrieval process. Our experiments show that BigPT outperforms state-of-the-art baselines in terms of program accuracy. Using our novel querying and retrieving mechanism, BigPT can be scaled to the large dataset and efficiently retrieve the translation.

Binger Chen

Technische Universität Berlin

Ziawasch Abedjan

Leibniz Universität Hannover

Machine Learning is a vital part of various modern day decision making software.
At the same time, it has shown to exhibit bias, which can cause an unjust treatment of individuals and population groups. One method to achieve fairness in machine learning software is to provide individuals with the same degree of benefit, regardless of sensitive attributes (e.g., students receive the same grade, independent of their sex or race). However, there can be other attributes that one might want to discriminate against (e.g., students with homework should receive higher grades). We will call such attributes anti-protected attributes. When reducing the bias of machine learning software, one risks the loss of discriminatory behaviour of anti-protected attributes. To combat this, we use grid search to show that machine learning software can be debiased (e.g., reduce gender bias) while also improving the ability to discriminate against anti-protected attributes.

Max Hort

University College London

Federica Sarro

University College London

United Kingdom

Automation in quantum software testing is essential to support systematic and cost-effective testing. Towards this direction, we present a quantum software testing tool called Quito that can automatically generate test suites covering three coverage criteria defined on inputs and outputs of a quantum program coded in Qiskit, i.e., input coverage, output coverage, and input-output coverage. Quito also implements two types of test oracles based on program specifications, i.e., checking whether a quantum program produced a wrong output or checking a probabilistic test oracle with statistical test. We describe the architecture and methodology of the tool. We also validated the tool with one quantum program and one faulty version of it. Results indicate that Quito can generate test suites and perform test assessments that detect faults, and produce test results with a good time performance.

Xinyi Wang

Nanjing University of Aeronautics and Astronautics, Nanjing, China

China

Paolo Arcaini

National Institute of Informatics

Japan

Tao Yue

Nanjing University of Aeronautics and Astronautics

China

Shaukat Ali

Simula Research Laboratory, Norway

Norway

Many code changes that developers make in their projects are repeated and constitute recurrent change patterns. It is of interest to collect such patterns from the version history of open-source repositories and suggest the most useful of them as quick fixes. In this paper, we present Revizor — a tool aimed to build custom plugins for PyCharm, a popular Python IDE. A Revizor-based plugin can take recurrent change patterns and highlight potential places for their application in the developer’s code editor. If the developer accepts the quick fix, the plugin automatically performs the edit. Our approach uses a graph-based representation of code changes, which allows us to support complex distributed code patterns. We have also asked several experienced developers to rate the usability and the performance of our plugin, and they gave us a positive feedback.

The source code of the tool and test plugin prototype are available on GitHub: https://github.com/JetBrains-Research/revizor A demonstration video with a short tool description can be found on YouTube: https://youtu.be/5eLs14nco7E

Oleg Smirnov

JetBrains Research, Saint Petersburg State University

Artyom Lobanov

JetBrains Research

Yaroslav Golubev

JetBrains Research

Russia

Elena Tikhomirova

JetBrains Research

Timofey Bryksin

JetBrains Research; HSE University

Russia