Towards Understanding Machine Learning Testing in Practise

Visualisations drive all aspects of the Machine Learning (ML) Development Cycle but remain a vastly untapped resource by the research community. ML testing is a highly interactive and cognitive process which demands a human-in-the-loop approach. Besides writing tests for the code base, bulk of the evaluation requires application of domain...

Maintaining and Monitoring AIOps Models Against Concept Drift

AIOps solutions enable faster discovery of failures in operational large-scale systems through machine learning models trained on operation data. These models become outdated during the occurrence of concept drift, a term used to describe shifts in data distributions. In operation data concept drift is inevitable and it impacts the...

'Project smells' - Experiences in Analysing the Software Quality of ML Projects with mllint

Machine Learning (ML) projects incur novel challenges in their development and productionisation over traditional software applications, though established principles and best practices in ensuring the project's software quality still apply. While using static analysis to catch code smells has been shown to improve software quality attributes...

Code Smells for Machine Learning Applications

The popularity of machine learning has wildly expanded in recent years. Machine learning techniques have been heatedly studied in academia and applied in the industry to create business value. However, there is a lack of guidelines for code quality in machine learning applications. In particular, code smells have rarely been studied in this...

Are Concept Drift Detectors Reliable Alarming Systems?: A Comparative Study

As machine learning models increasingly replace traditional business logic in the production system, their lifecycle management is becoming a significant concern. Once deployed into production, the machine learning models are constantly evaluated on new streaming data. Given the continuous data flow, shifting data, also known as concept drift,...

Assessing Robustness of ML-Based Program Analysis Tools using Metamorphic Program Transformations

Metamorphic testing is a well-established testing technique that has been successfully applied in various domains, including testing deep learning models to assess their robustness against data noise or malicious input. Currently, metamorphic testing approaches for machine learning (ML) models focused on image processing and object recognition...

The Prevalence of Code Smells in Machine Learning Projects

Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the current computer science landscape. Yet, there still exists a lack of software engineering experience and best practices in this field. One such best practice, static code analysis, can be used to find code smells, i.e., (potential) defects in the source code,...

AI lifecycle models need to be revised: An exploratory study in Fintech

Tech-leading organizations are embracing the forthcoming artificial intelligence revolution. Intelligent systems are replacing and cooperating with traditional software components. Thus, the same development processes and standards in software engineering ought to be complied in artificial intelligence systems. This study aims to understand the...