Explainable Priority Assessment of Software-Defects using Categorical Features at SAP HANA

• We want to automate priority assessment of software defects. To do so we provide a tool which uses an explainability-driven framework and classical machine learning algorithms to keep the decisions transparent. Differing from other approaches we only use objective and categorical fields from the bug tracking system as features. This makes our approach lightweight and extremely fast. We perform binary classification with priority labels corresponding to deadlines. Additionally, we evaluate the tool on real data to ensure good performance in the practical use case. © 2020 ACM.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Keyword

bug priority

defect assessment

machine learning

software quality

Defects

Learning algorithms

Binary classification

Bug tracking system

Categorical features

Practical use

Priority assessment

Software defects

Software engineering

Publication and Content Type

ref (subject category)

kon (subject category)