Data-Preparation for Machine-Learning Based Static Code Analysis: Unterschied zwischen den Versionen
Keine Bearbeitungszusammenfassung |
Keine Bearbeitungszusammenfassung |
||
Zeile 5: | Zeile 5: | ||
|betreuer=Robert Heinrich | |betreuer=Robert Heinrich | ||
|termin=Institutsseminar/2022-04-01 | |termin=Institutsseminar/2022-04-01 | ||
|kurzfassung= | |vortragsmodus=online | ||
|kurzfassung=Static Code Analysis (SCA) has become an integral part of modern software development, especially since the rise of automation in the form of CI/CD. It is an ongoing question of how machine learning can best help improve SCA's state and thus facilitate maintainable, correct, and secure software. However, machine learning needs a solid foundation to learn on. This thesis proposes an approach to build that foundation by mining data on software issues from real-world code. We show how we used that concept to analyze over 4000 software packages and generate over two million issue samples. Additionally, we propose a method for refining this data and apply it to an existing machine learning SCA approach. | |||
}} | }} |
Aktuelle Version vom 11. März 2022, 10:51 Uhr
Vortragende(r) | Felix Griesau | |
---|---|---|
Vortragstyp | Masterarbeit | |
Betreuer(in) | Robert Heinrich | |
Termin | Fr 1. April 2022 | |
Vortragsmodus | online | |
Kurzfassung | Static Code Analysis (SCA) has become an integral part of modern software development, especially since the rise of automation in the form of CI/CD. It is an ongoing question of how machine learning can best help improve SCA's state and thus facilitate maintainable, correct, and secure software. However, machine learning needs a solid foundation to learn on. This thesis proposes an approach to build that foundation by mining data on software issues from real-world code. We show how we used that concept to analyze over 4000 software packages and generate over two million issue samples. Additionally, we propose a method for refining this data and apply it to an existing machine learning SCA approach. |