Search based algorithms for test sequence generation in functional testing

Abstract

Context: The generation of dynamic test sequences from a formal specification, complementing traditional testing methods in order to find errors in the source code. Objective: In this paper we extend one specific combinatorial test approach, the Classification Tree Method (CTM), with transition information to generate test sequences. Although we use CTM, this extension is also possible for any combinatorial testing method. Method: The generation of minimal test sequences that fulfill the demanded coverage criteria is an NP-hard problem. Therefore, search-based approaches are required to find such (near) optimal test sequences. Results: The experimental analysis compares the search-based technique with a greedy algorithm on a set of 12 hierarchical concurrent models of programs extracted from the literature. Our proposed search-based approaches (GTSG and ACOts) are able to generate test sequences by finding the shortest valid path to achieve full class (state) and transition coverage. Conclusion: The extended classification tree is useful for generating of test sequences. Moreover, the experimental analysis reveals that our search-based approaches are better than the greedy deterministic approach, especially in the most complex instances. All presented algorithms are actually integrated into a professional tool for functional testing.

Extended Covering Arrays for Sequence Coverage

“…The corresponding proposed model checker can verify the validity of test sequences by combining I/O interval specifications and CCTL expressions. Some similar coverage requirements with t-way testing criteria were proposed by Kruse [1,12], such as state coverage, transition coverage and state pair coverage. The state coverage is similar to the 1-wise sequence coverage, which needs all the states to be covered at least once, and constraints among test cases involving the order of states in every factor need to be avoided.…”

“…Failure means that the system operates with unexpected behaviors. Testing is a very necessary and significant means of system quality assurance during the product development life cycle [1]. A report released by NIST (National Institute of Standards and Technology) in 2002 stated that software system bugs cost the U.S. economy 59.5 billion dollars annually [2].…”

“…In practice, SUTs always involve constraints or dependencies in or among test cases [1], such as interaction {b 1 , c 1 } must not appear in a test case or value c 2 must not be input after c 1 . If a test does not meet the constraints, the test is invalid.…”

Artificial Bee Colony and Neural Networks Optimization Test Generation Algorithm for Multiple Faults of Digital Circuits

“…Experimental results on ISCAS'85 circuits show that the algorithm can quickly obtain multiple stuck-at faults' test generation vector, comparing with other algorithm; the test generation efficiency is improved obviously. The two stuck-at-0(s-a-0) faults are on V 1 , V 2 and the two stuck-at-1(s-a-1) faults are on V 3 , V 4 .In order to convert the multiple stuck-at faults to single stuck-at fault, two additional gates should be inserted [3] , one is on-line gate and the other is fault gate. A． On-line gates: A two-input gate should be inserted in each faulty line.…”

An Optimal Solution for Test Case Generation Using ROBDD Graph and PSO Algorithm

“…One of the most common reasons, which leads to the software failure, is the faults in its requirements. However, most of the researches in the software testing literature concentrate on software code testing . Because the number of possible input parameter combinations in software requirements is massive, it may result in combinatorial explosion .…”