The software clone detection is an active research area, which is very important for software maintenance, bug detection etc. The two pieces of cloned code reflect some similarities or equivalents in the syntax or structure of the code representations. There are many representations of code like AST, token, PDG etc. The PDG (Program Dependency Graph) of source code can contain both syntactic and structural information. However, most existing PDG-based tools have high time consuming and miss many clones because they detect code clones with exact graph matching by using subgraph isomorphism. In this paper, we propose a novel PDG-based code clone detector, CCGraph, that uses graph kernels. Firstly, we normalize the structure of PDGs and design a two-stage filtering strategy by measuring the characteristic vectors of codes. Then we detect the code clones by using approximate graph matching algorithm based on the reforming WL (Weisfeiler-Lehman) graph kernel. Experiment results show that CCGraph retains a high accuracy, has both better recall and F1-score values, and detects more unique clones than other two related state-of-the-art tools. Besides, CCGraph is much more efficient than the existing PDG-based tools.

Yue Zou

Department of Computer Science and Technology, University of Science and Technology of China

Bihuan Ban

School of Data Science, University of Science and Technology of China

Yinxing Xue

Yun Xu

University of Science and Technology of China

China

The existing concurrency model for Java (or C) requires programmers to design and implement thread-safe classes by explicitly acquiring locks and releasing locks. Such a model is error-prone and is the reason for many concurrency bugs. While there are alternative models like transactional memory, manually writing locks remains prevalent in practice. In this work, we propose AutoLock, which aims to solve the problem by fully automatically generating thread-safe classes. Given a class which is assumed to be correct with sequential clients, AutoLock automatically generates a thread-safe class which is linearizable and does it in a way without requiring a specification of the class. AutoLock takes three steps: (1) infer access annotations (i.e., abstract information on how variables are accessed and aliased), (2) synthesize a locking policy based on the access annotations, and (3) consistently implement the locking policy. AutoLock has been evaluated on a set of benchmark programs and the results show that AutoLock generates thread-safe classes effectively and could have prevented existing concurrency bugs.

Haichi Wang

College of Intelligence and Computing, Tianjin University

Zan Wang

Tianjin University, China

China

Jun Sun

Singapore Management University

Singapore

Shuang Liu

Tianjin University, China

Ayesha Sadiq

Monash University

Yuan-Fang Li

Monash University

Australia

JavaScript is one of the most popular programming languages. WeChat Mini-Program is a large ecosystem of JavaScript applications that run on the WeChat platform. Millions of Mini-Programs are accessed by WeChat users every week. The performance and robustness of Mini-Programs are particularly important. Unfortunately, many Mini-Programs suffer from various defects and performance problems. Dynamic analysis is a useful technique to pinpoint application defects. However, due to the dynamic features of the JavaScript language and the complexity of the runtime environment, dynamic analysis techniques were rarely used to improve the quality of JavaScript applications running on industrial platforms such as WeChat Mini-Program. In this work, we report our experience of extending Jalangi, a dynamic analysis framework for JavaScript applications developed by academia, and applying the extended version, named WeJalangi, to diagnose defects in WeChat Mini-Programs. WeJalangi is compatible with existing dynamic analysis tools such as DLint and JITProf. We implemented a null pointer checker on WeJalangi and tested the tool’s usability on 152 open-source Mini-Programs. We also conducted a case study in Tencent by applying WeJalangi on six popular commercial Mini-Programs. In the case study, WeJalangi accurately located six null pointer issues and three of them haven’t been discovered previously. All of the reported defects have been already confirmed by developers and testers.

Yi Liu

Southern University of Science and Technology

Jinhui Xie

Tencent Inc.

Jianbo Yang

Tencent Inc.

Shiyu Guo

Tencent Inc.

Yuetang Deng

Tencent, Inc.

Shuqing Li

Southern University of Science and Technology

Yechang Wu

Southern University of Science and Technology

Yepang Liu

Southern University of Science and Technology