Spectrum Based Fault Localization (SBFL) is a statistical approach to identify faulty code within a program given a program spectra (i.e., records of program elements executed by passing and failing test cases). Several SBFL techniques have been proposed over the years, but most evaluations of those techniques were done only on Java and C programs, and frequently involve artificial faults. Considering the current popularity of Python, indicated by the results of the Stack Overflow survey among developers in 2020, it becomes increasingly important to understand how SBFL techniques perform on Python projects. However, this remains an understudied topic. In this work, our objective is to analyze the effectiveness of popular SBFL techniques in real-world Python projects. We also aim to compare our observed performance on Python to previously-reported performance on Java. Using the recently-built bug benchmark BugsInPy as our fault dataset, we apply five popular SBFL techniques (Tarantula, Ochiai, OP, Barinel, and DStar) and analyze their performances. We subsequently compare our results with results from Java and C projects reported in earlier related works.

We find that 1) the real faults in BugsInPy are harder to identify using SBFL techniques compared to the real faults in Defects4J, indicated by the lower performance of the evaluated SBFL techniques on BugsInPy; 2) older techniques such as Tarantula, Barinel, and Ochiai consistently outperform newer techniques (i.e., OP and DStar) in a variety of metrics and debugging scenarios; 3) claims in preceding studies done on artificial faults in C and Java (such as ``OP outperforms Tarantula") do not hold on Python real faults; 4) lower-performing techniques can outperform higher-performing techniques in some cases, emphasizing the potential benefit of combining SBFL techniques. Our results yield insight into how popular SBFL techniques perform in real Python faults and emphasize the importance of conducting SBFL evaluations on real faults.