A Comparative Study on Test Case Generation of Concurrent Programs

This paper deals with a comparative study on testing of concurrent programs based on different techniques. The various challenges in testing concurrent programming are: defining test coverage criteria based on control flow, generating control flow graph of nondeterministic programs, investigating the applicability of sequential testing criteria to parallel program testing etc. For solving these issues, some existing techniques are discussed in this study. Various researchers use an intermediate graph called Event Inter Actions Graph (EIAG) to solve the problem of generating the control flow graph of nondeterministic programs. Some researches propose an intermediate graph called Interaction Sequence Testing Criteria (ISTC) approach based on sequence of interactions to solve the problem of test coverage criteria based on control and data flow. Another method to solve the problem of generating test coverage based on control flow graph of nondeterministic programs is constraint based approach. It needs constrained elements to generate test case which includes structural element and constraint. The selection of good test cases has been addressed by test data generation technique. The technique of concurrent path analysis approach is used to solve the problem of applicability of sequential testing criteria to parallel program testing. It reduces the number of combined concurrent test paths. The sequential test paths are combined to form concurrent test path. The Integration and System Test Automation (ISTA) approach is used to solve the problem of applicability of sequential testing criteria to parallel program testing. It is used for automated test case generation and execution by using high-level Petri net is a finite state test model.

Source Code Prioritization Using Forward Slicing for Exposing Critical Elements in a Program

Even after thorough testing, a few bugs still remain in a program with moderate complexity. These residual bugs are randomly distributed throughout the code. We have noticed that bugs in some parts of a program cause frequent and severe failures compared to those in other parts. Then, it is necessary to take a decision about what to test more and what to test less within the testing budget. It is possible to prioritize the methods and classes of an object-oriented program according to their potential to cause failures. For this, we propose a program metric called influence metric to find the influence of a program element on the source code. First, we represent the source code into an intermediate graph called extended system dependence graph. Then, forward slicing is applied on a node of the graph to get the influence of that node. The influence metric for a method m in a program shows the number of statements of the program which directly or indirectly use the result produced by method m. We compute the influence metric for a class c based on the influence metric of all its methods. As influence metric is computed statically, it does not show the expected behavior of a class at run time. It is already known that faults in highly executed parts tend to more failures. Therefore, we have considered operational profile to find the average execution time of a class in a system. Then, classes are prioritized in the source code based on influence metric and average execution time. The priority of an element indicates the potential of the element to cause failures. Once all program elements have been prioritized, the testing effort can be apportioned so that the elements causing frequent failures will be tested thoroughly. We have conducted experiments for two well-known case studies -- Library Management System and Trading Automation System -- and successfully identified critical elements in the source code of each case study. We have also conducted experiments to compare our scheme with a related scheme. The experimental studies justify that our approach is more accurate than the existing ones in exposing critical elements at the implementation level.

Modified condition decision coverage criteria for test suite prioritization using particle swarm optimization

Purpose–Test suite prioritization technique is the process of modifying the order in which tests run to meet certain objectives.Early fault detection and maximum coverage of source code are the main objectives of testing.There are several test suite prioritization approaches that have been proposed at the maintenance phase of software development life cycle.A few works are done on prioritizing test suites that satisfy modified condition decision coverage(MC/DC)criteria which are derived for safety-critical systems.The authors know that it is mandatory to do MC/DC testing for Level A type software according to RTCA/DO178C standards.The paper aims to discuss this issue.Design/methodology/approach–This paper provides a novel method to prioritize the test suites for a system that includes MC/DC criteria along with other important criteria that ensure adequate testing.Findings–In this approach,the authors generate test suites from the input Java program using concolic testing.These test suites are utilized to measure MC/DC%by using the coverage calculator algorithm.Now,use MC/DC%and the execution time of these test suites in the basic particle swarm optimization technique with a modified objective function to prioritize the generated test suites.Originality/value–The proposed approach maximizes MC/DC%and minimizes the execution time of the test suites.The effectiveness of this approach is validated by experiments on 20 moderate-sized Java programs using average percentage of fault detected metric.