Watch out for extrinsic bugs!: A case study of their impact in just-in-time bug prediction models on the OpenStack Project

• Gema Rodríguez-Pérez ^[1] ; Meiyappan Nagappan ^[2] ; Gregorio Robles ^[3]
  1. [1] University of British Columbia

     University of British Columbia

     Canadá

     
  2. [2] University of Waterloo

     University of Waterloo

     Canadá

     
  3. [3] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     

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• Localización: Actas de las XXVII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2023) / coord. por Amador Durán Toro , 2023
• Idioma: inglés
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• Resumen

  • Intrinsic bugs are bugs for which a bug-introducing change can be identified in the version control system of a software. In contrast, extrinsic bugs are caused by external changes to a software, such as errors in external APIs; thereby they do not have an explicit bug-introducing change in the version control system. Although most previous research literature has assumed that all bugs are of intrinsic nature, in a previous study, we show that not all bugs are intrinsic. This paper shows an example of how considering extrinsic bugs can affect software engineering research. Specifically, we study the impact of extrinsic bugs in Just-In-Time bug prediction by partially replicating a recent study by McIntosh and Kamei on JIT models. These models are trained using properties of earlier bug-introducing changes. Since extrinsic bugs do not have bug-introducing changes in the version control system, we manually curate McIntosh and Kamei’s dataset to distinguish between intrinsic and extrinsic bugs. Then, we address their original research questions, this time removing extrinsic bugs, to study whether bug-introducing changes are a moving target in Just-In-Time bug prediction. Finally, we study whether characteristics of intrinsic and extrinsic bugs are different. Our results show that intrinsic and extrinsic bugs are of different nature. When removing extrinsic bugs the performance is different up to 16 percent Area Under the Curve points. This indicates that our JIT models obtain a more accurate representation of the real world. We conclude that extrinsic bugs negatively impact Just-In-Time models. Furthermore, we offer evidence that extrinsic bugs should be further investigated, as they can significantly impact how software engineers understand bugs.