Affect Recognition in Code Review: An In-situ Biometric Study of Reviewer’s Affect

https://apic.id/jurnal/index.php/jsc/article/download/37/23

The Performance of Fuzzy Logic How to Fixed The Risk Level of Bug System as Hard-working Quality Software

Characterizing Individual Gaze Patterns of Pair Programming Participants

The investigation of human attention networks on debugging performance

Bir programlama süreci içerisinde karşılaşılan olumsuz durumlara hata; hataların fark edilmesi, aranması, bulunması, çözümlenmesi ve tekrar oluşmaması için önlem alınması işlemlerine hata ayıklama denir. Bir programlama sürecinde farklı türde hatalar (derleme zamanı hataları, çalışma zamanı hataları ve mantık hataları) ile karşılaşılmaktadır. Her hata farklı özelliklere sahip olmakla birlikte bireylerin hata giderirken sergiledikleri davranışlar araştırmalarda ilgi çekicidir. Hata ayıklarken bireylerin bilişsel performanslarına ilişkin çıkarım yapmak için göz izleme yöntemi kullanılır. Araştırmalarda kullanılan göz hareket metrikleri birbirlerinden farklıdır ve neden kullanıldığına yönelik belirsizlik vardır. Bu çalışmanın amacı farklı türde hata ayıklamada önemli göz hareketlerinin belirlenmesidir. Araştırmaya mesleki ve teknik liselerin Bilişim Teknolojileri Bölümlerinde okuyan 51 öğrenci katılmıştır. Öğrencilerin hata ayıklama performanslarını belirlemek için Hata Ayıklama Performansı Testi kullanılmıştır. Göz hareketlerini belirlemek için Gazepoint GP3 göz izleme aracı kullanılmıştır. Araştırmanın analizlerinde ilgi alanları belirlenmiş ve göz hareketleri, makine öğrenmesinde kullanılan öznitelik belirleme yöntemlerinden olan Bilgi Kazancı ve Gini Katsayısı ile incelenmiştir. Araştırma sonucunda öğrencilerin farklı türde hata ayıklarken sergiledikleri önemli göz hareketlerinin hata türlerinin özelliklerini yansıttığı belirlenmiştir. Son olarak, bu sonuca göre önerilerde bulunulmuştur.

Öznitelik Belirleme Yöntemleriyle Farklı Türde Hata Ayıklamada Önemli Göz Hareket Metriklerinin Belirlenmesi

Debugging is one of the important links in software quality assurance. Generally, the debugging performance of people adopting different debugging strategies varies enormously. Although there are studies discussing debugging strategies, little research analyzes the impact of these strategies towards different types of bugs. In this paper, the experiments conducted on 20 participants suggest that there do exist differences on the eye movement data of those successful and failed debugging samples. Specifically, concerning data flow bugs, it is beneficial to pay attention to the changes of variables, Nevertheless, it is more important to watch the code and understand their logical structure when dealing with control flow bugs. We believe it can help programmers find defects more efficiently by combining this conclusion and the error message provided by the compiler.

An Eye Tracking Research on Debugging Strategies towards Different Types of Bugs

An Eye Tracking Research on Debugging Strategies towards Different Types of Bugs.

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An Eye Tracking Research on Debugging Strategies towards Different Types of Bugs

An Eye Tracking Research on Debugging Strategies towards Different Types of Bugs.