Changes are occurred frequently during continuous integration. Existing testing methods often suffer from weak specificity or insufficiency when applied to continuous integration. To solve this problem, we implement a fuzzing tool called CIDFuzz for continuous integration. First, difference analysis is performed to locate the change points, and the distances between basic blocks and the change points are calculated. Then, the distances are instrumented into the program under test. During fuzz testing, testing resources are allocated according to the coverage of seeds to test the change points effectively.

Fuzz testing is a widely used technique to detect software defects and vulnerabilities. Coverage-guided fuzzing aims to improve code coverage by generating offspring test cases through mutation, executing the program under test, and retaining interesting seeds for subsequent mutations using customized genetic algorithms. However, existing fuzzing tools rarely consider the similarity between seeds during mutation. Mutating similar seeds frequently generates similar offspring test cases, which results in similar coverage and reduces the efficiency of fuzz testing. To alleviate the impact of this problem on fuzz testing, this paper proposes a fuzz testing method based on seed diversity analysis, which focuses on the characteristics of seeds and uses byte sequences as a feature to measure the similarity between seeds. It collects seeds that can cover new edges and constructs a shorter seed queue with significant differences based on this feature, which replaces the original seed queue for mutation. Based on the proposed method, we implement the prototype tools AFL-Varied and Neuzz-Varied. Compared with AFL and Neuzz on six projects, the edge coverage and basic block coverage can be increased by 214.57% and 233.33 % at most, respectively.

As agile software development and extreme programing have become increasingly popular, continuous integration (CI) has become a widely used collaborative work method. However, it is common to make changes frequently to a project during CI. If existing testing methods are applied to CI directly, it will be difficult to make testing resources focus on changes generated by CI, which results in insufficient testing for changes. To solve this problem, we propose a fuzz testing method for CI. First, differential analysis is performed to determine the change points generated during CI, change points are added to the taint source set, and static analysis is conducted to calculate the distances between each basic block and the taint sources. Then, the project under test is instrumented according to the distances. During fuzz testing, testing resources are allocated based on seed coverage to test the change points effectively. Using the proposed methods, we implement CIDFuzz as a prototype tool, and experiments are conducted on four open-source projects that use CI. Experimental results show that, compared with AFL and AFLGo, CIDFuzz can reduce the time costs of covering change points up to 39.59% and 41.64%, respectively. Also, CIDFuzz can reduce the time costs of reproducing vulnerabilities up to 34.78% and 25.55%.

Smart contracts are computer programs running on the blockchain, and their security issues have received widespread attentions. However, existing vulnerability detection techniques such as static analysis, symbol execution, and fuzz testing could be unable to expose new types of vulnerabilities in time because they rely on extracting vulnerability features manually. On the other hand, vulnerability detection techniques based on machine learning do not require experts to define features manually and can learn vulnerability patterns automatically. In this paper, we propose a vulnerability detection technique for smart contracts, which extracts smart contract features based on the abstract syntax tree (AST) and trains the model to detect smart contract vulnerabilities. Experiment results show that the model achieves an average accuracy of 98.7% and F1-value of 0.885 in detecting three kinds of security vulnerabilities.

With the widespread use of agile software development methods, such as agile and scrum, software is iteratively updated more frequently. To ensure the quality of the software, regression testing is conducted before new versions are released. Moreover, to improve the efficiency of regression testing, testing efforts should be concentrated on the modified and impacted parts of a program. However, the costs of manually constructing new test cases for the modified and impacted parts are relatively expensive. Fuzz testing is an effective method for generating test data automatically, but it is usually devoted to achieving higher code coverage, which makes fuzz testing unsuitable for direct regression testing scenarios. For this reason, we propose a fuzz testing method based on the guidance of historical version information. First, the differences between the program being tested and the last version are analyzed, and the results of the analysis are used to locate change points. Second, change impact analysis is performed to find the corresponding impacted basic blocks. Finally, the fitness values of test cases are calculated according to the execution traces, and new test cases are generated iteratively by the genetic algorithm. Based on the proposed method, we implement a prototype tool DeltaFuzz and conduct experiments on six open-source projects. Compared with the fuzzing tool AFLGo, AFLFast and AFL, DeltaFuzz can reach the target faster, and the time taken by DeltaFuzz was reduced by 20.59%, 30.05% and 32.61%, respectively.

Fuzz testing has been successful in finding defects of various software packages. These defects include file parsing, image processing, Internet browsers, and network protocols. However, the quality of the initial seed test cases greatly influences the coverage and defect detection capability of fuzz testing. To address this issue, we propose CSEFuzz, a fuzz testing approach based on symbolic execution for defect detection. First, CSEFuzz generates candidate test cases by symbolic execution and collects coverage information of the test cases. Then, CSEFuzz extracts the test-case templates of the test cases and selects a set of test-case templates according to specific coverage criteria. Finally, CSEFuzz selects test cases according to the selected test-case templates, and the selected test cases are used as initial seed test cases for fuzz testing. Experiments are conducted on 11 open-source programs. The results show that in comparison with afl-cmin, which is the test-case selection command of Kelinci, CSEFuzz with a path coverage criterion reduces the time costs of the initial seed test selection and verification by 94.26%. In addition, compared with afl-cmin, 32 more paths are covered and 16 more defects are detected by CSEFuzz.