Analyzing a Semantics-Aware Bug Seeding Tool's Efficacy: A qualitative study with the SemSeed tool

  • Vinícius Martins PUC-Rio
  • Camila T. Ramalho PUC-Rio
  • Lucas Cordeiro Marques PUC-Rio
  • Juliana Alves Pereira PUC-Rio
  • Alessandro Garcia PUC-Rio
  • Carlos Lucena PUC-Rio
  • Bruno Feijó PUC-Rio
  • Antonio L. Furtado PUC-Rio

Resumo


Software developers can benefit from machine learning solutions to predict bugs. Machine learning solutions usually require a lot of data to train a model in order to achieve reliable results. In this context, developers use bug-seeding approaches to generate synthetic bugs, which should be similar to human-made bugs. A recent state-of-the-art tool, called SemSeed, uses a semantics-aware bug seeding approach in order to hopefully achieve more realistic bugs. In this study, we report on the investigation of SemSeed’s efficacy. We create a survey that shows developers a bug and asks whether it is a Real or Synthetic bug. We collected and analyzed the answers from 47 developers, and we show that SemSeed can be very accurate in seeding realistic bugs.

Palavras-chave: software engineering, machine learning, bug seeding, bug detection
Publicado
25/09/2023
MARTINS, Vinícius; RAMALHO, Camila T.; MARQUES, Lucas Cordeiro; PEREIRA, Juliana Alves; GARCIA, Alessandro; LUCENA, Carlos; FEIJÓ, Bruno; FURTADO, Antonio L.. Analyzing a Semantics-Aware Bug Seeding Tool's Efficacy: A qualitative study with the SemSeed tool. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SOFTWARE (SBES), 37. , 2023, Campo Grande/MS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 246–256.