Fuzz testing automatically generates and executes test cases, to detect more defects by covering more logical and state spaces of the program under test (PUT).However, it becomes more difficult to adequately test the PUT with increasing size and code complexity.Studies have shown that complex code is more likely to contain defects, and the loop is one of the main reasons for increased code complexity.Therefore, it is necessary to thoroughly test the loops, but existing fuzzers cannot focus on the loops of the PUT.To address this issue, we design a loop interval coverage metric to measure the testing adequacy of the loop.Additionally, we propose a greybox fuzz testing approach named AFL 2 oop (AFL for Loop), which uses loop coverage as guidance.First, we analyze the loops of the PUT and expand the bitmap.Then, fuzz testing is guided by loop interval coverage and branch coverage.A prototype tool is implemented based on the proposed method, and experiments are carried out on four real-world software programs, such as LibXml2, LibMing, etc.The results show that AFL 2 oop achieves higher coverage, triggers more crashes, and reproduces defects faster than AFL and FairFuzz.

Software fault localization, which is an important software quality assurance technology, provides the location of the faults in software to improve the efficiency of debugging and repairing. In previous research, software fault localization techniques, such as spectrum-based, mutation-based, and program slicing, have been widely used and achieved good results. However, many statements could have same suspicious values by using these techniques, which will consume large amount of manual effort to confirm and affect the accuracy of fault localization. For example, using Ochiai or DStar to locate 395 faulty versions of 6 projects in Defects4J, nearly 70 % of the faulty versions have more than one suspicious statement are ranked as top tied 1. To address the above problem, this paper proposes a complexity-based fault localization (CBFL) technique to further improve the accuracy of fault localization. Firstly, a set of metrics for measuring the complexity of statements is proposed, and the metrics of each statement in projects are extracted to construct a classification model. Then, the classification model is used to predict the faulty probability of the statements which are ranked as top tied 1 by SBFL, MBFL or other techniques, and these statements are reranked according to the estimated faulty probability to improve the accuracy of fault localization. This paper implements a fault localization tool CDStar based on the CBFL, and conducts experiments on the Defects4J dataset. Comparing with the DStar, the results show that CBFL outperforms DStar in terms of Einspect @1 and EXAM.