DeepAI AI Chat
Log In Sign Up

An empirical study of Linespots: A novel past-fault algorithm

by   Maximilian Scholz, et al.

This paper proposes the novel past-faults fault prediction algorithm Linespots, based on the Bugspots algorithm. We analyze the predictive performance and runtime of Linespots compared to Bugspots with an empirical study using the most significant self-built dataset as of now, including high-quality samples for validation. As a novelty in fault prediction, we use Bayesian data analysis and Directed Acyclic Graphs to model the effects. We found consistent improvements in the predictive performance of Linespots over Bugspots for all seven evaluation metrics. We conclude that Linespots should be used over Bugspots in all cases where no real-time performance is necessary.


page 1

page 2

page 3

page 4


An Empirical Study of Fault Localization in Python Programs

Despite its massive popularity as a programming language, especially in ...

Learning Test-Mutant Relationship for Accurate Fault Localisation

Context: Automated fault localisation aims to assist developers in the t...

An Empirical Study of Fault Localization Families and Their Combinations

The performance of fault localization techniques is critical to their ad...

Combining Spreadsheet Smells for Improved Fault Prediction

Spreadsheets are commonly used in organizations as a programming tool fo...

DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System

In modern industrial systems, diagnosing faults in time and using the be...

Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization

There is growing importance to detecting faults and implementing the bes...

Predictive Maintenance in Photovoltaic Plants with a Big Data Approach

This paper presents a novel and flexible solution for fault prediction b...