A Reinforcement Learning Approach for Control Flow Error Detection in Automated Software Testing

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Marmara Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği (İngilizce) Anabilim Dalı, Türkiye

Tezin Onay Tarihi: 2022

Tezin Dili: İngilizce

Öğrenci: ENGİN DURMAZ

Danışman: Mustafa Borahan Tümer

Özet:

Automatic software testing methods have become inevitable in producing test inputs. Test tools generate events and execute these events on SUT to find a bug. Unfortunately, when a crash occurs, an event sequence which includes many irrelevant events to the failure is obtained. These irrelevant events make debugging process more difficult. To locate and repair bugs with an emphasis on the crash scenarios, we present in this work a reinforcement learning (RL) approach for finding the shortest input sequence(s) leading to system crash or block where these represent the goal state of the RL problem. We shoot for simplifying the bug scenario as much as possible so that developers would analyze agent’s actions causing crashes or freeze. In this study, we developed Crash Detection Module (CDM ) which consists of three main flows. We first simplify the crash scenario given using Recursive Delta Debugging (RDD), then we apply RL algorithms to explore for the shorter crashing sequence. We approach the exploration of crash scenarios as a RL problem where the agent first attains the goal state of crash/blocking by executing inputs, then shortens the input sequence with the help of the rewarding mechanism. We apply both model-free on-policy and model-based planning-capable RL agents to our problem. Furthermore, we present a novel RL approach, involving Detected Goal Catalyst (DGC), which decreases the time complexity by avoiding grappling with convergence via stopping learning at a small variance and attaining the shortest crash sequence with an algorithm that recursively removes unrelated actions. Experiments show that DGC significantly improves the learning performance of both SARSA and Prioritized Sweeping algorithms on finding the shortest path.