• Led the Software PErformance And Reliability (SPEAR) Lab as the principal investigator.• Ranked as one of the most active early-career Software Engineering researchers in the world by an independent study published in the Journal of Systems and Software. • Several mentees are now tenure-track assistant professors at universities worldwide (e.g., the University of Alberta, York University, DePaul University, and IIT Gandhinagar). • Extensive industry experience. Research already used on a daily basis in practice (i.e., BlackBerry, Ericsson, Microsoft, and ERA). • Received multiple prestigious awards, such as the Concordia Research Award and Queen's Research Achievement Award.

• Guided Ericsson developers on the application of AI/ML in their development projects, enhancing their technical capabilities.• Assisted Ericsson developers in integrating log analytics into their software quality assurance practices, improving defect detection and resolution.• Developed machine learning-based techniques for capacity planning, demonstrating how AI can enhance resource management and operational efficiency. • Applied machine learning techniques to identify library upgrade patterns, enabling more accurate and timely push notifications.• Developed tools that improved Ericsson developers' library upgrade suggestions by over 70%, significantly enhancing their development process.

• Developed an ensemble of unsupervised anomaly detection algorithms to automate the verification of user inputs, significantly reducing manual efforts and improving data quality for stakeholders.• Led the design and integration of an AI-based input verification framework into a traditional non-AI enterprise software system.• Addressed challenges in operationalizing anomaly detection from diverse perspectives, including dynamic datasets, business contexts, and the interplay between human expertise and AI.• Provided insights and solutions for mitigating challenges in anomaly validation, enhancing the adoption of AI models in business settings.

===Research Highlights===*Testing and Continuous Integration*• Continuous Integration (CI) Testing: Conducted large-scale longitudinal studies on CI test execution and failure, offering valuable insights into test reliability and its impact on software quality.• Test Redundancy and Impact Analysis: Identified and optimized test redundancy, and investigated test impact analysis focusing on code dependencies, improving test execution and maintenance strategies.• Disabled and Flaky Tests: Investigated common patterns and causes of disabled and flaky tests in CI environments. Provided actionable recommendations to manage and mitigate the impact of these tests. • Test Smells and Maintenance: Conducted empirical studies on test smells, their evolution, and maintenance. Identified common test smells, their impact on maintenance, and provided guidelines for refactoring and maintaining high-quality test suites.*Log Analysis and Bug Localization*• Log Analysis Tools: Developed tools to enhance log analysis and debugging, providing organized and concise views of logs, and improving runtime execution path visibility.• Bug Localization: Improved bug localization techniques by reconstructing execution paths using logs, significantly increasing accuracy and efficiency.• LLM-Based Log Parsing: Utilized large language models to improve log parsing accuracy, enabling better extraction of useful information from log data.*Code Generation*• LLM-Based Code Generation: Explored the use of large language models for code generation, improving the efficiency and accuracy of code synthesis from natural language descriptions. This research advances the capability of LLMs to assist in software development by automatically generating code snippets that align with developer intents and requirements.• LLM-Based Test Code Generation and Quality Assurance: Focused on generating test code using large language models, enhancing the automation of test case creation.

I am a member of the SEMLA research group, where we focus on research related to SE for AI/ML.https://semla.quebec/en/

Programming languages used: Java, Python, R• Conducted research on performance engineering and log analysis.• Applied program analysis approaches to detect performance problems in database-centric applications.• Applied data mining, machine learning, and statistical techniques to discover the relationships between software concerns (such as functionality) and software defects.• Studied text mining and information retrieval, and applied these techniques to help improve software quality.• Written tools in Python for statistical analysis and data processing.• Used the R language for statistical programming and data mining.

Programming languages used: Java, Python, R, Perl• Led, designed, and implemented a static bug detection framework for detecting database-related bugs.• Led, designed, and implemented an autonomic real-time system configuration optimization framework that helped improve system performance by over 30%.• Improved system memory usage on database servers by over 50% using program analysis.• Developed a tool for debugging the performance problems of ORM-generated SQLs.• Designed and implemented a log analysis framework using MongoDB and different data mining approaches.• Created test cases and tools for performance testing.

Programming languages used: Java, C++• Explored and used various open source systems and integrated them with the project.• Participated in the design decision of the User Interface.• Implemented data visualizations by using both Java and ActionScript 3.0.• Leveraged graph structure to represent relationships among data and to utilize space in the memory .• Written scripts in Python to parse and generate XML files for data transfer.• Integrated JavaScript with Actionscript 3.0 and Flex.• Developed software using the Scrum approach.

• Developed good communication skill by tutoring first year computer science students 1-on-1.• Explained concepts of OOP and programming conventions patiently.• Developed great patience by explaining basic concepts to students in a detailed and clear manner.