Combining model-based and combinatorial testing for effective test case generation

Nguyen, Cu; Marchetto, Alessandro; Tonella, Paolo

doi:10.1145/2338965.2336765

Cited by 67 publications

(49 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…systems are non-deterministic, the main focus of FSMbased testing work has been on deterministic FSMs and these have been found to be sufficient in important application domains such as hardware [24], protocol conformance testing [5], [25], [26], [27], [28], [29], objectoriented systems [30], web services [31], [32], [33], [34], and general software [35].…”

Section: Finite State Machines (Fsms)mentioning

confidence: 99%

Parallel Algorithms for Testing Finite State Machines:Generating UIO Sequences

Hierons

Türker

2016

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Abstract-This paper describes an efficient parallel algorithm that uses many-core GPUs for automatically deriving Unique Input Output sequences (UIOs) from Finite State Machines. The proposed algorithm uses the global scope of the GPU's global memory through coalesced memory access and minimises the transfer between CPU and GPU memory. The results of experiments indicate that the proposed method yields considerably better results compared to a single core UIO construction algorithm. Our algorithm is scalable and when multiple GPUs are added into the system the approach can handle FSMs whose size is larger than the memory available on a single GPU.

show abstract

Section: Finite State Machines (Fsms)mentioning

confidence: 99%

Parallel Algorithms for Testing Finite State Machines:Generating UIO Sequences

Hierons

Türker

2016

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

show abstract

“…The main restriction we make is that we consider deterministic FSMs. The main focus of FSM-based testing has been on deterministic FSMs and these have been used in areas such as hardware [47], protocol conformance testing [5], [8], [9], [11], object-oriented systems [12], web services [13], [48], [49], and general software [50].…”

Section: Finite State Machines (Fsms)mentioning

confidence: 99%

Parallel Algorithms for Generating Harmonised State Identifiers and Characterising Sets

Hierons

Türker

2016

IEEE Trans. Comput.

View full text Add to dashboard Cite

Abstract-Many automated finite state machine (FSM) based test generation algorithms require that a characterising set (CS) or a set of harmonised state identifiers (HSIs) is first produced. The only previously published algorithms for partial FSMs were brute-force algorithms with exponential worst case time complexity. This paper presents polynomial time algorithms and also massively parallel implementations of both the polynomial time algorithms and the brute-force algorithms. In the experiments the parallel algorithms scaled better than the sequential algorithms and took much less time. Interestingly, while the parallel version of the polynomial time algorithm was fastest for most sizes of FSMs, the parallel version of the brute-force algorithm scaled better due to lower memory requirements.

show abstract

“…Crawljax can be seen as a learning-based GUI testing technique that uses ad-hoc criteria for state-merging. Recently, a number of model-learning based testing techniques targeting Android application [6,33,36,40,45] have been proposed. Nguyen et al's approach [33] combines offline model-based testing and combinatorial testing to refine created test cases.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, a number of model-learning based testing techniques targeting Android application [6,33,36,40,45] have been proposed. Nguyen et al's approach [33] combines offline model-based testing and combinatorial testing to refine created test cases. Yang et al [45] take online model-based testing approach similar to Crawljax [29].…”

Section: Related Workmentioning

confidence: 99%

Guided GUI testing of android apps with minimal restart and approximate learning

Choi

Necula

Sen

2013

Proceedings of the 2013 ACM SIGPLAN International Conference on Object Oriented Programming Systems Languages &Amp; Application

192

View full text Add to dashboard Cite

Smartphones and tablets with rich graphical user interfaces (GUI) are becoming increasingly popular. Hundreds of thousands of specialized applications, called apps, are available for such mobile platforms. Manual testing is the most popular technique for testing graphical user interfaces of such apps. Manual testing is often tedious and error-prone. In this paper, we propose an automated technique, called SwiftHand, for generating sequences of test inputs for Android apps. The technique uses machine learning to learn a model of the app during testing, uses the learned model to generate user inputs that visit unexplored states of the app, and uses the execution of the app on the generated inputs to refine the model. A key feature of the testing algorithm is that it avoids restarting the app, which is a significantly more expensive operation than executing the app on a sequence of inputs. An important insight behind our testing algorithm is that we do not need to learn a precise model of an app, which is often computationally intensive, if our goal is to simply guide test execution into unexplored parts of the state space. We have implemented our testing algorithm in a publicly available tool for Android apps written in Java. Our experimental results show that we can achieve significantly better coverage than traditional random testing and L * -based testing in a given time budget. Our algorithm also reaches peak coverage faster than both random and L * -based testing.

show abstract

Combining model-based and combinatorial testing for effective test case generation

Cited by 67 publications

References 26 publications

Parallel Algorithms for Testing Finite State Machines:Generating UIO Sequences

Parallel Algorithms for Testing Finite State Machines:Generating UIO Sequences

Parallel Algorithms for Generating Harmonised State Identifiers and Characterising Sets

Guided GUI testing of android apps with minimal restart and approximate learning

Contact Info

Product

Resources

About