Empirical Analysis of Hidden Technical Debt Patterns in Machine Learning Software
Paper i proceeding, 2019

Context/Background
Machine Learning (ML) software has special ability for increasing technical debt due to ML-specific issues besides having all the problems of regular code. The term “Hidden Technical Debt” (HTD) was coined by Sculley et al. to address maintainability issues in ML software as an analogy to technical debt in traditional software.
Goal
The aim of this paper is to empirically analyse how HTD patterns emerge during the early development phase of ML software, namely the prototyping phase. 
Method
Therefore, we conducted a case study with subject systems as ML models planned to be integrated into the software system owned by Västtrafik, the public transportation agency in the west area of Sweden.
Results
During our case study, we could detect HTD patterns, which have the potential to emerge in ML prototypes, except for “Legacy Features”, “Correlated features”, and “Plain Old Data Type Smell”.
Conclusion
Preliminary results indicate that emergence of significant amount of HTD patterns can occur during prototyping phase. However, generalizability of our results require analyses of further ML systems from various domains.

Machine learning

Software maintainability

Hidden Technical Debt

Författare

Mohannad Alahdab

Cybercom Sweden

Student vid Chalmers

Gul Calikli

Chalmers, Data- och informationsteknik, Software Engineering, Software Engineering for Cyber Physical Systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 11915 LNCS 195-202

20th International Conference on Product-Focused Software Process Improvement, PROFES 2019
Barcelona, Spain,

Ämneskategorier

Annan data- och informationsvetenskap

Programvaruteknik

Datavetenskap (datalogi)

DOI

10.1007/978-3-030-35333-9_14

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Senast uppdaterat

2021-10-21