Sequans Communications Enhances QA with an ML-Powered SBA

Sequans Communications accelerated fault detection and improved test efficiency using Tieto Tech Consulting's machine learning solution for automated log analysis in 5G/4G chipset testing.

Benefits

Log analysis time reduction

10x

KPIs analyzed

200

Test campaigns run

Executive summary

Goal: Improve the efficiency, speed, and precision of system log analysis to identify faults and deviations in Sequans Communications’ continuous integration testing of 5G/4G IoT chipsets.
Solution: Delivered a custom machine learning-based SBA solution that detects deviations from normal system behavior in real time by analyzing system logs and performance metrics.
Benefits: Faster fault detection, improved test accuracy, reduced manual effort, and deeper insight into system anomalies that were not previously detectable.
Services Delivered: Data analytics, AI solutions.

About the customer

Global semiconductor company specializing in producing secure and scalable 4G/5G chipsets and modules for cellular IoT

Sequans Communications leads in LTE-M/NB-IoT, Cat 1bis, and 5G RedCap/eRedCap platforms, delivering low-power connectivity for massive IoT, broadband, and mission-critical applications. Headquartered in France, Sequans Communications operates worldwide with offices across Europe, the US, and Asia.

Business challenge

Customer sought a smart, automated log analysis solution

Sequans Communications manages a large-scale, automated test environment to validate the behavior of its chipsets across complex telecom scenarios. To identify faults, performance issues, or anomalies, the company has a test log analysis procedure, which requires significant time when done manually. The logs, being the primary source of data, can be unstructured and complex to analyze. Besides, missing subtle deviations can impact end-product quality and time-to-market.

To ensure higher accuracy, faster fault resolution, and enhanced test cycle efficiency, Sequans Communications sought a smart, automated log analysis solution capable of identifying deviations from normal behavior across multiple campaign scenarios.

Solution

System Behavior Analysis implemented

Tieto Tech Consulting implemented a System Behavior Analysis (SBA) solution for Sequans Communications based on our in-house machine learning research. We introduced a new temporal and sequential learner, capable of understanding log context and pinpointing anomalies with minimal false positives. The approach trains ML models using logs from successful test runs to establish a baseline of normal behavior. Using this baseline, the model identifies both deviations and their exact timing, allowing faster debugging and improved precision.

Business value

Log analysis time reduced from hours or days to minutes

With minimal manual tuning, SBA can parse complex logs, detect patterns, and significantly cut down analysis time – from hours or days to minutes – helping experts to focus on important tasks and improving product quality.

Key Benefits:

Time savings: Log analysis time (including training and parsing) reduced from hours/days to minutes, saving engineers’ time.
Higher accuracy: The solution detects anomalies previously missed.
Increased product quality: More reliable fault detection ensures better-performing chipsets.
Secure by design: On-premises deployment ensures that no sensitive data leaves the client's infrastructure.
Scalability: Initially integrated with 3 test campaigns, expanding to 60.

Customer quote

The solution delivered by Tieto Tech Consulting engineers gave us insights we couldn't see before. It accelerated our fault detection and showed real potential from the very first proof of concept. Now, in the next phases of SBA implementation, we are planning to integrate it into other test campaigns to maximize the impact across the validation process. We see this as a strategic step toward faster releases and higher product quality.

Xianbo Meng, Product Validation Manager at Sequans Communications

Technical details

From a proof of concept to a multi-phase enagement

The project began with a 4-week proof of concept, using Sequans Communications’ logs to validate solution accuracy. Next, it evolved into a multi-phase engagement:

Phase 1: As an extension of our initial PoC, we conducted the initial analysis across three log sources and over 200 KPIs. Sequans Communications immediately observed faster and accurate fault detection and the ability to uncover hidden issues.
Phase 2 (currently in progress): Focus is made on integration of the solution into Sequans Communications’ environment.
Coming up next: SBA will be scaled across more test campaigns (up to 60).

The solution will also support sensitive data handling by operating fully on-premises, ensuring data security and compliance.

Technologies used

Python
Machine Learning
Angular (for frontend)
Typescript (for frontend)
Anomaly detection
KPI analysis
Log analysis

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