Data Structures and Algorithms (DS) is a basic computer science course that is a prerequisite for taking advanced information systems (IS) curriculum courses. The course aims to teach students how to analyze a problem, design a solution, and implement it using pseudocode to construct knowledge and develop the necessary skills for algorithmic problem solving and abstract thinking. While the literature acknowledges the difficulty of this course, few references were found that examine the process students undergo while solving DS algorithmic problems. The study's objective is to explore and describe IS students' problem-solving processes and challenges requiring a high level of abstract thinking in a "black box" approach. During the study, 13 students were observed while solving a complex problem, using "think aloud" (TA) techniques. Each observation was recorded, transcribed, and iteratively analyzed using principles of provisional coding in qualitative data analysis. The findings suggest that the quality and correctness of the solutions depend on three main factors: abstract thinking, flexibility applied during the solution process, and an absence of misconceptions related to concepts and the basic understanding of the problem. The students' levels of abstract thinking also influenced the quality of visualization used while trying to solve the problem. This study's findings may raise the awareness of DS course designers and instructors regarding the importance of the role of abstract thinking, possible misconceptions, and strategies used in problem solving as factors influencing students' ability to solve complex problems.