Browsing by Author "Mukhtarov, Ziya"

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    The Great Dog Massacre in late Ottoman İstanbul: The history of “Hayırsız Ada”
    (Bilkent University, 2020) Abdullazade, Sama; Gasimova, Minaya; Mammadlı, Shahla; Mukhtarov, Ziya; Baghirov, Javid; Ghasemlou, Kimya
    In this paper, the exile of 80 000 street dogs of Istanbul to Sivriada in 1910, later known as “Hayırsız Ada”, will be studied from different aspects. There were multiple failed attempts at collecting dogs from Istanbul streets until 1910 due to the local pressure. However, in 1910, street dogs in the Ottoman Empire were being collected by the municipality to be sold to France, which ended up with France withdrawing from the contract. As a result, the dogs were left to their fate in Sivriada. This paper carefully analyzes the history of actions that led up to the banishment of the dogs, the condition of the dogs in Sivriada, the reaction after the dogs died, and how this act is still being remembered today.
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    Towards better code reviews: using mutation testing to improve reviewer attention
    (IEEE, 2023-07-06) Mukhtarov, Ziya; Abdul, Mannan; Raupova, Mokhlaroyim; Baghirov, Javid; Tanveer, Osama; Altunel, Haluk; Tüzün, Eray
    Code reviews, while effective, can be crippled by process smells if not performed correctly. A typical process smell that harms the efficacy of code reviews is the ‘Looks Good To Me’ (LGTM) smell, wherein a reviewer approves a code review task without reviewing the code attentively. Low-quality code reviews can be harmful, as they can cause bugs to slip into a product codebase leading to potentially severe consequences. In this paper, we propose an innovative solution to potentially minimize the occurrence of the LGTM smell commonly found in code reviews. We built a tool that is a proof-of-concept implementation of our solution, which incorporates the concept of mutation testing into code reviews. It provides a platform where pull request authors can apply mutations to the pull request code in GitHub. Reviewer attention and review efficacy are measured based on their mutation score. To the best of our knowledge, our proof of concept implementation is the first-ever code review tool that uses the concept of mutation testing. We validated our proposed solution with eight developers and received promising results.