Highlights of research profile and impact:
- Dr. Garousi has published more than 145 papers, so far in his career.
- According to the Google Scholar, his h-index is 31 (as of June 2020).
- In 2018, a bibliometric study published in the Journal of Systems and Software, ranked Dr. Garousi as the 8th most-active "consolidated" software engineering researcher world-wide, among about 4,000+ active researchers in software engineering who have published in top-quality journals.
A few selected recent papers:
Research collaboration between industry and academia supports improvement and innovation in industry and helps ensure the industrial relevance of academic research. However, many researchers and practitioners in the community believe that the level of joint industry-academia collaboration (IAC) projects in Software Engineering (SE) research is relatively low, creating a barrier between research and practice.
The goal of the empirical study reported in this paper was to explore and characterize the state of IAC with respect to industrial needs, developed solutions, impacts of the projects and also a set of challenges, patterns and anti-patterns identified by a recent Systematic Literature Review (SLR) study.
According to various reports, many software engineering (SE) graduates often face difficulties when beginning their careers, which is mainly due to misalignment of the skills learned in university education with what is needed in the software industry.
We performed a meta-analysis to aggregate the results of the set of 35 studies published in this area, as of 2018, to provide a consolidated view on how to align SE education with industry needs, to identify the most important skills and also existing knowledge gaps.
Via an industry–academia collaboration, we developed a test automation framework for aviation simulation software. The technology has been successfully deployed in several test teams. We have observed that the test automation framework has been instrumental in helping test engineers test more effectively and efficiently by enabling them to write robust test scripts that are easy to maintain.
In collaboration with an industry partner: HAVELSAN A.Ş.
This work was motivated by a real industrial need to improve regression-testing practices in the context of a safety-critical industrial software in the aviation domain. To address our objective, we set up and conducted an “action-research” collaborative project between industry and academia.
Using a conceptual multi-objective regression-test selection framework (called MORTO), we developed a custom-built genetic algorithm (GA). The GA is able to provide full coverage of the affected (changed) requirements while considering multiple cost and benefit factors of regression testing. e.g., minimizing the number of test cases, and maximizing cumulative number of detected faults by each test suite.
The empirical results of applying the approach on the Software Under Test (SUT) demonstrated that the approach could yield a more efficient test suite (in terms of costs and benefits) compared to the old (manual) test-selection approach, used in the company, and another applicable approach chosen from the literature. The new approach is now actively utilized in the company under study. With this new approach, regression selection process in the project under study is not ad-hoc anymore. Furthermore, we have been able to eliminate the subjectivity of regression testing and its dependency on expert opinions.