Hashing fuzzing: introducing input diversity to improve crash detection

The utility of a test set of program inputs is strongly influenced by its diversity and its size. Syntax coverage has become a standard proxy for diversity. Although more sophisticated measures exist, such as proximity of a sample to a uniform distribution, methods to use them tend to be type dependent. We use r-wise hash functions to create a novel, semantics preserving, testability transformation for C programs that we call HashFuzz. Use of HashFuzz improves the diversity of test sets produced by instrumentation-based fuzzers. We evaluate the effect of the HashFuzz transformation on eight programs from the Google Fuzzer Test Suite using four state-of-the-art fuzzers that have been widely used in previous research. We demonstrate pronounced improvements in the performance of the test sets for the transformed programs across all the fuzzers that we used. These include strong improvements in diversity in every case, maintenance or small improvement in branch coverage – up to 4.8% improvement in the best case, and significant improvement in unique crash detection numbers – between 28% to 97% increases compared to test sets for untransformed program

Hashing Fuzzing: Introducing Input Diversity to Improve Crash Detection

The utility of a test set of program inputs is strongly influenced by its diversity and its size. Syntax coverage has become a standard proxy for diversity. Although more sophisticated measures exist, such as proximity of a sample to a uniform distribution, methods to use them tend to be type dependent. We use r-wise hash functions to create a novel, semantics preserving, testability transformation for C programs that we call HashFuzz. Use of HashFuzz improves the diversity of test sets produced by instrumentation-based fuzzers. We evaluate the effect of the HashFuzz transformation on eight programs from the Google Fuzzer Test Suite using four state-of-the-art fuzzers that have been widely used in previous research. We demonstrate pronounced improvements in the performance of the test sets for the transformed programs across all the fuzzers that we used. These include strong improvements in diversity in every case, maintenance or small improvement in branch coverage -- up to 4.8% improvement in the best case, and significant improvement in unique crash detection numbers -- between 28% to 97% increases compared to test sets for untransformed programs

Policy Decomposition for Evaluation Performance Improvement of PDP

In conventional centralized authorization models, the evaluation performance of policy decision point (PDP) decreases obviously with the growing numbers of rules embodied in a policy. Aiming to improve the evaluation performance of PDP, a distributed policy evaluation engine called XDPEE is presented. In this engine, the unicity of PDP in the centralized authorization model is changed by increasing the number of PDPs. A policy should be decomposed into multiple subpolicies each with fewer rules by using a decomposition method, which can have the advantage of balancing the cost of subpolicies deployed to each PDP. Policy decomposition is the key problem of the evaluation performance improvement of PDPs. A greedy algorithm with O(nlgn) time complexity for policy decomposition is constructed. In experiments, the policy of the LMS, VMS, and ASMS in real applications is decomposed separately into multiple subpolicies based on the greedy algorithm. Policy decomposition guarantees that the cost of subpolicies deployed to each PDP is equal or approximately equal. Experimental results show that (1) the method of policy decomposition improves the evaluation performance of PDPs effectively and that (2) the evaluation time of PDPs reduces with the growing numbers of PDPs

Hashing fuzzing: introducing input diversity to improve crash detection

The utility of a test set of program inputs is strongly influenced by its diversity and its size. Syntax coverage has become a standard proxy for diversity. Although more sophisticated measures exist, such as proximity of a sample to a uniform distribution, methods to use them tend to be type dependent. We use r-wise hash functions to create a novel, semantics preserving, testability transformation for C programs that we call HashFuzz. Use of HashFuzz improves the diversity of test sets produced by instrumentation-based fuzzers. We evaluate the effect of the HashFuzz transformation on eight programs from the Google Fuzzer Test Suite using four state-of-the-art fuzzers that have been widely used in previous research. We demonstrate pronounced improvements in the performance of the test sets for the transformed programs across all the fuzzers that we used. These include strong improvements in diversity in every case, maintenance or small improvement in branch coverage – up to 4.8% improvement in the best case, and significant improvement in unique crash detection numbers – between 28% to 97% increases compared to test sets for untransformed program

Automated Realistic Test Input Generation and Cost Reduction in Service-centric System Testing

Service-centric System Testing (ScST) is more challenging than testing traditional software due to the complexity of service technologies and the limitations that are imposed by the SOA environment. One of the most important problems in ScST is the problem of realistic test data generation. Realistic test data is often generated manually or using an existing source, thus it is hard to automate and laborious to generate. One of the limitations that makes ScST challenging is the cost associated with invoking services during testing process. This thesis aims to provide solutions to the aforementioned problems, automated realistic input generation and cost reduction in ScST. To address automation in realistic test data generation, the concept of Service-centric Test Data Generation (ScTDG) is presented, in which existing services used as realistic data sources. ScTDG minimises the need for tester input and dependence on existing data sources by automatically generating service compositions that can generate the required test data. In experimental analysis, our approach achieved between 93% and 100% success rates in generating realistic data while state-of-the-art automated test data generation achieved only between 2% and 34%. The thesis addresses cost concerns at test data generation level by enabling data source selection in ScTDG. Source selection in ScTDG has many dimensions such as cost, reliability and availability. This thesis formulates this problem as an optimisation problem and presents a multi-objective characterisation of service selection in ScTDG, aiming to reduce the cost of test data generation. A cost-aware pareto optimal test suite minimisation approach addressing testing cost concerns during test execution is also presented. The approach adapts traditional multi-objective minimisation approaches to ScST domain by formulating ScST concerns, such as invocation cost and test case reliability. In experimental analysis, the approach achieved reductions between 69% and 98.6% in monetary cost of service invocations during testin

Exploring regression testing and software product line testing - research and state of practice

In large software organizations with a product line development approach a selective testing of product variants is necessary in order to keep pace with the decreased development time for new products, enabled by the systematic reuse. The close relationship between products in product line indicates an option to reduce the testing effort due to redundancy. In many cases test selection is performed manually, based on test leaders’ expertise. This makes the cost and quality of the testing highly dependent on the skills and experience of the test leaders. There is a need in industry for systematic approaches to test selection. The goal of our research is to improve the control of the testing and reduce the amount of redundant testing in the product line context by applying regression test selection strategies. In this thesis, the state of art of regression testing and software product line testing are explored. Two extensive systematic reviews are conducted as well as an industrial survey of regression testing state of practice and an industrial evaluation of a pragmatic regression test selection strategy. Regression testing is not an isolated one-off activity, but rather an activity of varying scope and preconditions, strongly dependent on the context in which it is applied. Several techniques for regression test selection are proposed and evaluated empirically but in many cases the context is too specific for a technique to be easily applied directly by software developers. In order to improve the possibility for generalizing empirical results on regression test selection, guidelines for reporting the testing context are discussed in this thesis. Software product line testing is a relatively new research area. The understanding about challenges is well established but when looking for solutions to these challenges, we mostly find proposals, and empirical evaluations are sparse. Regression test selection strategies proposed in literature are not easily applicable in the product line context. Instead, control may be increased by increased visibility of the effects of testing and proper measurements of software quality. Focus of our future work will be on how to guide the planning and assessment of regression testing activities in large, complex reuse based systems, by visualizing the quality achieved in different parts of the system and evaluating the effects of different selection strategies when applied in various regression testing situations