Goshgar Can Ismayilov has successfully defended his PhD thesis.

Barış Yamansavaşçılar has successfully defended his PhD thesis

Professor Lale Akarun and her research team at Boğaziçi University have been awarded computing hours by EuroHPC

Yiğit Yıldırım has successfully defended his PhD thesis

The Boğaziçi University Outstanding Achievement Award in Research, given annually by the Boğaziçi University Foundation (BÜVAK), has found its owners.

Sümeyye Ağaç has successfully defended her PhD thesis

Our PhD graduate, Dr. Alper Ahmetoğlu, received the BAP PhD Thesis Award.

Berrenur Saylam has successfully defended her PhD thesis

Beytullah Yiğit has successfully defended her PhD thesis

Nuriye Özlem Özcan Şimşek has successfully defended his PhD thesis

Two of our graduate students, Merve Gül Kantarcı and Timoteos Onur Özçelik, won the second and third prizes for the best student paper at the Signal Processing and Communication Applications conference held in Tarsus between May 14-18. The Alper Atalay Best Student Paper Award at the Signal Processing and Communications Applications Conference is given in memory of Alper Atalay, who graduated with honors from Boğaziçi University, Department of Electrical and Electronics Engineering in 1993 and lost his life in a tragic car accident on August 16, 1998. ...

Binnur Görer has successfully defended her PhD thesis

Alper Ahmetoğlu has successfully defended his PhD thesis

The CMPE Graduation Ceremony 2023 was held on July 7.

Underestimating the value of software testing had catastrophic results in recent history. Automated Test Generation (ATG) is an approach that aims to minimize the manual effort required for testing. This thesis aims to improve the effectiveness and performance of ATG approaches via Machine Learning (ML) based guidance, and focuses on Android Graphical User Interface (GUI) testing using Reinforcement Learning (RL), specifically. We propose four solutions, Q-learning Based Exploration (QBE), Test Case Mutation (TCM), Fully Automated Reinforcement LEArning Driven (FARLEAD), and FARLEAD2 test generators. QBE uses RL to crawl a set of applications and learns an action generation policy while exploring. Then, it uses this learned policy to either detect more unique crashes or cover more activities in new applications. TCM takes the tests QBE generates and replaces the well-behaving actions in those tests with bad-behaving ones to detect even more crashes. FARLEAD uses RL to learn how to verify a functional behavior that is given as a high-level test scenario in the form of a monitorable formal specification. FARLEAD learns by trial-and-error like QBE but it learns app-specific patterns instead of QBE’s app-generic patterns. To the best of out knowledge, FARLEAD is the first engine fully automating the functional testing of GUI applications. Finally, FARLEAD2 improves FARLEAD with Generalized Experience Replay (GER) and human-readable Staged Test Scenario (STS) language.Experimental results show that, QBE outperforms state-of-the-art test generators in crash detection and coverage. Furthermore, executing QBE first and then switching to TCM detects even more unique crashes. FARLEAD and FARLEAD2 expand the scope of automated testing to verifying functional behavior. Overall, these test generators elevate automated GUI testing closer to replacing manual GUI testing. ...