Test Case Length Research Articles

Comparing Graph-Based Algorithms to Generate Test Cases from Finite State Machines

Model-Based Testing (MBT) is a well-known technique that employs formal models to represent reactive systems’ behavior and generates test cases. Such systems have been specified and verified using mostly Finite State Machines (FSMs). There is a plethora of test generation algorithms in the literature; most of them are based on graphs once an FSM can be formally defined as a graph. Nevertheless, there is a lack of studies on analyzing cost and efficiency of FSM-based test generation algorithms. This study compares graph-based algorithms adopted to generate test cases from FSM models. In particular, we compare the Chinese Postman Problem (CPP) and H-Switch Cover (HSC) algorithms with the well-known Depth-First Search (DFS) and Breadth-First Search (BFS) algorithms in the context of covering all-transitions and all-transition-pairs criteria in an FSM. First, a systematic literature mapping was conducted to summarize the methods that have been adopted in MBT, considering FSMs. Second, the main methods found were implemented and analyzed on randomly-generated FSMs, as well as real-world models that represent embedded systems of space applications. To make comparisons, we considered analyses in terms of cost (time), efficiency (mutant analysis) and characteristics of the generated test suites (number of test cases, average length of test cases, largest and smallest test cases, standard deviation and distribution of test cases). In general, CPP presented the best results in terms of number of test cases and test suite size. In addition, CPP also presented low distribution of average length compared to other algorithms.

GUI testing assisted by human knowledge: Random vs. functional

Software testing is a labor-intensive task in software development life-cycle. Human knowledge is useful in the practices of software testing, especially GUI testing. There are many strategies for GUI testing assisted by human knowledge, in which manual random testing and manual functional testing are two of widely used ones. In this paper, an empirical study is conducted to compare random testing and functional testing in order to provide guidelines for GUI testing. 234 participants were recruited to create thousands of random and functional test cases for open source GUI applications. Some of these test cases were selected with certain coverage criteria and then run on GUI applications to evaluate random testing and functional testing. We study three aspects on the two testing strategies: effectiveness, complementarity and impact of test case length. Some useful observations in the empirical study are: (1) Random testing is more effective in the early stage of testing on small applications and functional testing has more extensive applicability for testing large sized applications. (2) Random testing and functional testing exhibit some complementarity in our experiment. (3) Short test cases can reveal some faults more quickly and long test cases can reveal more faults lastingly.

Handling test length bloat

SUMMARYThe length of test cases is a little investigated topic in search‐based test generation for object‐oriented software, where test cases are sequences of method calls. Although intuitively longer tests can achieve higher overall code coverage, there is always the threat of bloat – a complex phenomenon in evolutionary computation, where the length abnormally grows over time. In this paper, we show that bloat indeed also occurs in the context of test generation for object‐oriented software. We present different techniques to overcome the problem of length bloat, and evaluate all possible combinations of these techniques using different starting lengths for the search. Experiments on a set of difficult search targets, selected from several open source and industrial projects, show that controlling bloat with the appropriate techniques can significantly improve the search performance. Copyright © 2013 John Wiley & Sons, Ltd.

Evaluating test suite characteristics, cost, and effectiveness of FSM-based testing methods

ContextTesting from finite state machines has been investigated due to its well-founded and sound theory as well as its practical application. There has been a recurrent interest in developing methods capable of generating test suites that detect all faults in a given fault domain. However, the proposal of new methods motivates the comparison with traditional methods. ObjectiveWe compare the methods that generate complete test suites from finite states machines. The test suites produced by the W, HSI, H, SPY, and P methods are analyzed in different configurations. MethodComplete and partial machines were randomly generated varying numbers of states, inputs, outputs, and transitions. These different configurations were used to compare test suite characteristics (number of resets, test case length) and the test suite length (i.e., the sum of the length of its test cases). The fault detection ratio was evaluated using mutation testing to produce faulty implementations with an extra state. ResultsOn average, the recent methods (H, SPY, and P) produced longer test cases but smaller test suites than the traditional methods (W, HSI). The recent methods generated test suites of similar length, though P produced slightly smaller test suites. The SPY and P methods had the highest fault detection ratios and HSI had the lowest. For all methods, there was a positive correlation between the number of resets and the test suite length and between the test case length and the fault detection ratio. ConclusionThe recent methods rely on fewer and longer test cases to reduce the overall test suite length, while the traditional methods produce more and shorter test cases. Longer test cases are correlated to fault detection ratio which favored SPY, though all methods have a ratio of over 92%.