Input Modeling Prioritization Using Statistically User Profile for Pairwise Test Case Generation with Constraints Handling

Pairwise testing is a widely used technique for software testing with reduce size of the test suite and able to detect interactions that trigger the system’s faults. In addition, pairwise testing test suites must be able to deal with constraints between input parameters and values. In current practice, selecting input parameters and values usually depends on tester skills that might not be sufficient. Input parameters and values modeling and tools for easily guiding and prioritizing the selection of optimal input parameters and values for the SUT is also required. In this work, we present an approach for prioritizing input parameters and values modeling using statistical user profile. Our approach is implemented in a tool called UPPTCT which provides ability to handle constraints on input parameters and values for pairwise testing in order to generate test cases. We conduct experiments to evaluate test case effectiveness and compare our tool with other renowned pairwise test generation and constraints handling tools. The experimental results show that the effectiveness of our approach is significantly more efficient and effective than random testing as large portion of reported defects with regard to statically user profile were caught by our approach. Furthermore, our tool performs better in some cases and performs comparable results for generating test cases upon input parameters and values for both with constraints handling and without constraints handling.Pairwise testing is a widely used technique for software testing with the reduced size of the test suite and able to detect interactions that trigger the system’s faults. In addition, pairwise testing test suites must be able to take constraints between input parameters and parameter values into account. In current practice, identifying and selecting input parameters and parameter values usually depends on tester skills that might not be sufficient. Input parameters and parameter values modeling and tools for easily guiding and prioritizing the selection of optimal input parameters and parameter values for the SUT is also required. In this work, we present an approach for prioritizing input parameters and parameter values modeling using statistical user profile. Our approach is implemented in a tool called UPPTCT which provides the ability to handle constraints on input parameters and parameter values for pairwise testing in order to generate test cases. We conduct experiments to evaluate test case effectiveness and compare our tool with other renowned pairwise test generation and constraints handling tools. The experimental results show that the effectiveness of our approach is significantly more efficient and effective than random testing as a large portion of reported defects with regard to statically user profile were caught by our approach. Furthermore, our tool performs better in some cases and performs comparable results for generating test cases upon input parameters and parameter values for both with constraints handling and without constraints handling

Combinatorial Interaction Testing for Automated Constraint Repair

Highly-configurable software systems can be easily adapted to address user’s needs. Modelling parameter configurations and their relationships can facilitate software reuse. Combinatorial Interaction Testing (CIT) methods are already often used to drive systematic testing of software system configurations. However, a model of the system’s configurations not conforming with respect to its software implementation, must be repaired in order to restore conformance. In this paper we extend CIT by devising a new search-based technique able to repair a model composed of a set of constraints among the various software system’s parameters. Our technique can be used to detect and fix faults both in the model and in the real software system. Experiments for five real-world systems show that our approach can repair on average 37% of conformance faults. Moreover, we also show it can infer parameter constraints in a large real-world software system, hence it can be used for automated creation of CIT models

Validation of Constraints Among Configuration Parameters Using Search-Based Combinatorial Interaction Testing

The appeal of highly-configurable software systems lies in their adaptability to users’ needs. Search-based Combinatorial Interaction Testing (CIT) techniques have been specifically developed to drive the systematic testing of such highly-configurable systems. In order to apply these, it is paramount to devise a model of parameter configurations which conforms to the software implementation. This is a non-trivial task. Therefore, we extend traditional search-based CIT by devising 4 new testing policies able to check if the model correctly identifies constraints among the various software parameters. Our experiments show that one of our new policies is able to detect faults both in the model and the software implementation that are missed by the standard approaches

ModuleDigger: an itemset mining framework for the detection of cis-regulatory modules

Background: The detection of cis-regulatory modules (CRMs) that mediate transcriptional responses in eukaryotes remains a key challenge in the postgenomic era. A CRM is characterized by a set of co-occurring transcription factor binding sites (TFBS). In silico methods have been developed to search for CRMs by determining the combination of TFBS that are statistically overrepresented in a certain geneset. Most of these methods solve this combinatorial problem by relying on computational intensive optimization methods. As a result their usage is limited to finding CRMs in small datasets (containing a few genes only) and using binding sites for a restricted number of transcription factors (TFs) out of which the optimal module will be selected. Results: We present an itemset mining based strategy for computationally detecting cis-regulatory modules (CRMs) in a set of genes. We tested our method by applying it on a large benchmark data set, derived from a ChIP-Chip analysis and compared its performance with other well known cis-regulatory module detection tools. Conclusion: We show that by exploiting the computational efficiency of an itemset mining approach and combining it with a well-designed statistical scoring scheme, we were able to prioritize the biologically valid CRMs in a large set of coregulated genes using binding sites for a large number of potential TFs as input