【正文】 Examining the reports and unsuccessful test cases, Submitting bug reports are automated. GUI smoke test cases and test oracles are generated. Fault seeding is used to evaluate fault detection techniques used. An adequate number of faults of each fault type are seeded fairly. The disadvantages are Some part of code are missed by smoke tests, Some of the bugs reported by DART are false positive, Overall effectiveness of DART depends on GUI ripper capabilities, Not available for industry based application testing, Faults that are not manifested on the GUI will go undetected 5. INCORPORATING EVENT CONTEXT Xun Yuan et al [1], developed a new criterion for GUI testing. They used a binatorial interaction testing technique. The main motivation of using binatorial interaction is to incorporate context and it also considers event binations, sequence length and include all possible event. Graph models are used and covering array is used to generate test cases which are the basis for binatorial interaction testing. A tool called GUITAR (GUI Testing Framework) is used for testing and this provides functionalities like generate test cases, execute test cases, verify correctness and obtain coverage reports. Initially using GUI ripper, a GUI application is converted into event graph and then the events are grouped depending on functionality and constraints are identified. Covering array is generated and test sequences are produced. Test cases are generated and executed. Finally coverage is puted and a test adequacy criterion is analyzed. The advantages are: contexts are incorporated, detects more faults when pared to the previous techniques used. The disadvantages are infeasible test cases make some test cases unexecutable, grouping events and identifying constraints are not automated. Figure 5. Testing Process 6. CONCLUSIONS In this paper, some of the various test case generation methods and various types of GUI testing adapted for different GUI applications and techniques are studied. Different approaches are being used under various testing environment. This study helps to choose the test case generation technique based on the requirements of the testing and it also helps to choose the type of GUI test to perform based on the application type such as open source software, industrial software and the software in which changes are checked in rapidly and continuously. REFERENCES [1] [2] Xun Yuan, Myra B. Cohen, Atif M. Memon, (2021) “GUI Interaction Testing: Incorporating Event Context”, IEEE Transactions on Software Engineering, vol. 99. A. M. Memon, M. E. Pollack, and M. L. Soffa, (2021) “Hierarchical GUI test case generation using automated planning”, IEEE Transactions on Software Engineering, Vol. 27, no. 2, pp. 144 155. X. Yuan and A. M. Memon, (2021) “Using GUI runtime state as feedback to generate test cases”, in International Conference on Software Engineering (ICSE), pp. 396405. X. Yuan, M. Cohen, and A. M. Memon, (2021) “Covering array sampling of input event sequences for automated GUI testing”, in International Conference on Automated Software Engineering (ASE), pp. 405408. X. Yuan, M. Cohen, and A. M. Memon, (2021) “Towards dynamic adaptive automated test generation for graphical user interfaces”, in First International Workshop on TESTing Techniques amp。 t, k, v), is an N k array on v symbols with the property that every N t subarray contains all ordered subsets of size t of the v symbols at least once. As shown in Figure 2, Initially EIG model is created which is then partitioned into groups of interacting events and then constraints are identified and used to generate abstract model for testing. Long test cases are generated using covering array sampling. Event sequences are generated and executed. If any event interaction is missed, then regenerate test cases and repeat the steps. The disadvantages are event partition and identifying constraints are done manually. Figure 2. Test Generation Using Covering Array . Dynamic Adaptive Automated test Generation Xun Yuan et al [5], suggested an algorithm to generate test suites with fewer infeasible test cases and higher event interaction coverage. Due to dynamic state based nature of GUIs, it is necessary and important to generate test cases based on the feedback from the execution of tests. The proposed framework uses techniques from binatorial interaction testing to generate tests and basis for binatorial interaction testing is a covering array. Initially smoke tests are generated and this is used as a seed to generate Event Semantic Interaction (ESI) relationships. Event Semantic Interaction Graph is generated from ESI. Iterative refinement is done through geic algorithm. An initial model of the GUI event interactions and an initial set of test sequences based on the model are generated. Then a batch of test cases are generated and executed. Code coverage is determined and unexecutable test cases are identified. Once the infeasible test cases are identified, it is removed and the model is updated and new batch of test cases are generated and the steps are followed till all the uncovered ESI relationships are covered. These automated test case generation process is shown in Figure 3. This automated test generation also provides validation for GUIs. The disadvantage