Analysis and Visualization of Complex Software System Traces

Screenshot 2023-06-13 at 10 33 13 AM

Team

Table of Contents

  1. Introduction
  2. Objectives
  3. Links

Introduction

The objective of this project is to analyze and visualize the traces of complex software systems used in FinTech applications to ensure compliance with expectations and improve the quality of the software. Passive testing techniques used by industrial software applications to observe natural system behavior in both production and test environments will produce log files containing millions of lines of data per working day hard to be manually analyzed by a QA engineer. Machine learning algorithms will be developed to automate the process of identifying anomalies and potential issues in the system through analysis of log files of system traces.

Problem Statement :

The software systems used in FinTech applications are highly complex, with a large number of interactions between different components. The logs generated by these systems contain a vast amount of data, making it difficult to manually analyze them and identify potential issues. In addition, traditional quality assurance techniques may not be sufficient to detect all types of issues, such as those related to performance, security, or compliance.

Objectives :

● To develop machine learning models for anomaly detection and issue identification in system traces
● To visualize the system traces using interactive dashboards to provide a human understanding of what is happening in the system
● To evaluate the effectiveness of the proposed approach on real-world FinTech applications

Methodology :

● Collect and preprocess the system traces generated by the FinTech application
● Develop machine learning models for anomaly detection and issue identification, such as clustering, classification, or regression models
● Implement interactive dashboards for visualizing the system traces and model outputs
● Evaluate the effectiveness of the proposed approach on real-world FinTech applications using appropriate metrics, such as precision, recall, F1-score, or AUC-ROC.

Expected Outcomes :

● An automated system for analyzing and visualizing complex software system traces using machine learning techniques
● Interactive dashboards for providing a human understanding of the system behavior
● Improved quality assurance processes for FinTech applications, resulting in higher quality software and better compliance with
expectations

Project Team :

Product Owners

Project Timeline :

● Week 1: Project kick-off meeting, Project Proposal Presentation, data collection, and preprocessing
● Week 2-4: Model development and implementation (clustering, classification, or regression models)
● Week 5: Dashboard development and implementation using a visualization tool (e.g., Dash, Tableau, Power BI, or D3.js)
● Week 6: Evaluation and testing on real-world FinTech applications using appropriate metrics (e.g., precision, recall, F1-score, or AUC-ROC)
● Week 7: Final report writing, Project presentation and dissemination of results

Project Risks :

● The project may not be able to develop a method that is able to handle system traces of all kinds of software applications that are unseen while learning.
● The project may not be able to produce an industrial-ready-to-use solution due to time constraints ( less than 2 months) and unexpected events in academia.
● The project may not be able to develop a tool that is easy to use. ( Due to aiming the end user as an QA engineer )

Conclusion :

In conclusion, this project aims to develop an automated system for analyzing and visualizing complex software system traces to improve the quality of FinTech applications. The proposed approach will leverage machine learning techniques to identify anomalies and potential issues in the system, and interactive dashboards will be used to provide a human understanding of the system behavior. We expect this project to have significant practical implications for FinTech companies, resulting in higher quality software and improved compliance with expectations.