Main Research Projects and Tools

Current Projects

Automated Test Data Generation (Tool) for the ARC system

This project presents an automated, model based test data generation approach for CPS system that have complex input and output structures, such as ARC (Adaptive Resource Control) system, which is dynamically and autonomously utilizes space and ground resources to meet the changing needs of SES customers. We present a customized data modeling approach for these kind of systems, and a tool that automatically generate test data and validates the results after test execution. The approach receives two inputs, field data and a data model, i.e., class diagram annotated with stereotypes and OCL constraints. The data model is used to tailor the implementation of the mutation operators according to the fault model for SUT (system under test). We introduced four kind of test data generation strategies, Random (RND), All Possible Target (APT), Search Based (SB), and Constraint Based (CB). Search based test data generation strategy outperform than Random and APT. We introduced a customized evolutionary algorithm, that guided by the search objectives to ensure the high coverage of classes, mutation operators, input/output constraints, and the output diversity. Our approach is scalable that can handle a huge amount of data to be generated. The size of the model for the ARC system under study includes 65 class diagrams with hundreds of attributes. The proposed approach can also handle different formats (i.e., text, spreadsheet, json) of the field data, and we can generate new test data in the required format for SUT. To generate the test data, we introduced eight new mutation operators that generate diverse data. The test oracles work as a mapping between the input and output by using OCL constraints.

Collaboration with:

@ Software Verification and Validation Lab (SnT), University of Luxembourg

@ SES (Société Européenne des Satellites), Betzdorf Luxembourg (Industrial Partner)

Future Projects Ideas

Based on patch files locate the vulnerable code and generate exploits

Generating exploits based on patch files is a sensitive and potentially harmful activity if not handled responsibly. It involves reverse-engineering the patches to understand the original vulnerabilities and then creating code that exploits these vulnerabilities. This process should only be conducted in a controlled, ethical, and legal manner, such as for educational purposes, within a legitimate security research context, or with explicit permission from software owners. More

Stop vulnerabilities transformation from one system to another system

Completed Projects

Test Smell Detection in the ARC System

During our research and collaboration with SES, we have discovered a promising direction to pursue for the ARC (Adaptive Resource Control) system. Detecting the major test smells in the context of multi-level testing framework and improving test case definition (at the Gauge level) by removing these test smells. Test smells are mainly some bad development practices that reflects poor software design and implementation choices in software testing. Over the last decade, test smells are heavily studied to measure their prevalence and impacts on test maintainability especially for Unit-Tests. We can come up with an idea to develop a tool for test smell detection to support multi-level BDD based testing frameworks (e.g., Gauge), which can also help ARC testing team to detect suspected test smells in the ARC system.

Collaborated with:

@ Software Verification and Validation Lab (SnT), University of Luxembourg

@ SES (Société Européenne des Satellites), Betzdorf Luxembourg (Industrial Partner)

Log-based Test Case Slicing (Tool)

Regression testing is arguably one of the most important activities in software testing. However, its cost-effectiveness and usefulness can be largely impaired by complex system test cases that are poorly designed (e.g., test cases containing multiple test scenarios combined into a single test case) and that require a large amount of time and resources to run. One way to mitigate this issue is decomposing such system test cases into smaller, separate test cases, each of them with only one test scenario and with its corresponding assertions, so that the execution time of the decomposed test cases is lower than the original test cases, while the test effectiveness of the original test cases is preserved.

Collaborated with:

@ Software Verification and Validation Lab (SnT), University of Luxembourg

Obfuscated Code Detection in Android Applications (Tool)

In Android applications, if the application is obfuscated, it is hard to retrieve the exact source code after applying reverse engineering techniques on it. In this project, we propose an approach which is based on clone detection technique and it can detect obfuscated code in Android applications very efficiently. We perform two experiments on different types of datasets including obfuscated and non-obfuscated application's source code. We successfully detected the obfuscated code of two types, "identifier-renaming" and "string-encryption" with a high accuracy of 95%. A comparative study with other state-of-the-art tools proves the efficiency of our approach. Experimental results show that our approach is reliable, efficient and can be implemented at a large-scale level.

Collaborated with: