Test Automation for Verifying Software’s Detectability for Rule Violations Name: Zhishuai Yao Supervisor: Pro. Jukka Manner Place: Varian Medical Systems Finland Oy

Outlines Overview and background Objectives of the thesis Design and implementation Results and conclusions Q & A

Overview and Background This thesis is done in a company which develops software for radiation therapy in cancer treatment Automated tests are created to verify the error detecting mechanism (“checking functions”) in the software

Overview and Background Radiation therapy – Using radiation beam to irradiate the tumor. – Requires high accuracy at tumor positioning and dosing the treatment

Overview and Background Treatment planning system (TPS) – Computerized application used for simulate the dose distribution in CT images – Various of inputs to the TPS increases the risk for radiation therapy

Objectives of the Thesis Implementing the tests is to: – High level: reducing the risk in radiation therapy – Low level: eliminating the errors in the TPS by verifying the “checking functions” in the application

Design and Implementation Testing target: “checking functions” – For each specific violation of rule, checking function throws error or warning message to notify the user Testing method: “black box testing” – Generate faulty cases to violate every predefined rules and check whether the correct error or warning message is throw by the “checking function”

Design and Implementation Challenges – Understand each rule (requirement) and find the proper parameter to violate the rule – Setting the criteria to the test – Short execution time and reusability (e.g. for regression testing)

Design and Implementation

Test procedures: – Importing prerequisite data – Running the checking function for original data No error or warning should be thrown – Modify specific parameter – Running the checking function again Expected error or warning should be thrown – Log the result

Results and Conclusions Automated test has covered 93 rules (requirements) by the time this thesis was finalized (currently more than 120 )

Results and Conclusions Associated warning or error is not shown. Non-related warning or error is shown in addition to the correct warning or error message. Corruption in data model dependency rule. Some of the mandatory attributes are not correctly configured in the system.

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