ScienceSoft Global
Healthcare
Finance
Reverse Engineering at ScienceSoft
When documentation is sparse, and the original developers can’t be reached, understanding legacy software can seem impossible. ScienceSoft excels in uncovering the functionality and architecture of decades-old legacy systems, enabling businesses to modernize, integrate, or optimize their applications without disrupting operations.
Our Approach to Decoding Legacy Systems
Fast and efficient exploration
We prioritize performance-critical components and those with frequent historical changes for reverse engineering, as they often reflect evolving business requirements and core functionality. Where applicable, we use automation tools to handle repetitive or time-intensive tasks, such as code analysis or dependency mapping, to streamline the reverse engineering process.
Reliability and high performance
Our phased approach isolates and addresses one functional layer or module at a time. We prioritize non-invasive techniques (such as static code analysis, user shadowing, and prototyping) to ensure operational continuity while safeguarding system integrity. We conduct any invasive analysis exclusively in controlled staging environments, protecting production systems from disruptions.
Pragmatic modernization paths
We identify short-term and long-term opportunities for legacy software improvement based on the respective business objectives and scalability needs. We may recommend targeted code refactoring, technology stack upgrades, or new integrations, focusing on areas that are likely to deliver the highest ROI.
Transparent documentation
To avoid repeated knowledge loss, we maintain thorough documentation throughout the reverse engineering process and continuously share it with your stakeholders. We also conduct knowledge transfer and training sessions to ensure that your internal teams are fully equipped to manage and evolve the system in the future.
Key Practices We Employ in Reverse Engineering
At the heart of our reverse engineering process is the manual exploration of the system’s codebase and architecture. However, we understand that relying solely on this method can be time-consuming and not always provide the desired results. Here's what we do to enhance the accuracy and depth of our analysis:
1
Interviewing stakeholders
We conduct structured interviews with key stakeholders, including business leaders, power users, IT managers, and any remaining developers. These discussions focus on uncovering hidden dependencies and non-obvious business logic of the legacy apps.
2
Observing system usage
Next, we shadow end users as they interact with the system in real-world scenarios. This hands-on observation helps us understand workflows, input and output relationships, and system behavior under varying conditions.
3
Visualizing code design
Once structural information is gathered, we create detailed diagrams to depict the system architecture, data flows, and dependency relationships. Visual representations make it easier to identify complex connections and provide stakeholders with a clear understanding of the system’s structure.
4
Modeling system behavior
Building on the insights from diagrams and analysis, we develop behavioral models to represent workflows and interactions. These models simplify dynamic functionality, offering a clear, high-level view of the system's operational logic and making it easier to track and analyze the interactions between components.
5
Simulating and prototyping
Finally, we can create simulations and prototypes of critical system components to validate our assumptions and refine our understanding. This step ensures that all findings are accurate, actionable, and aligned with real-world conditions, paving the way for further development or modernization.
When to combine reverse engineering and refactoring
Refactoring can be another valuable tool during system analysis. It helps clarify and improve the code’s readability and maintainability, often revealing hidden requirements and business logic. However, refactoring can bring significant additional costs. We evaluate the need for refactoring on a case-by-case basis, focusing on scenarios where it delivers clear value, such as when the code is overly complex or obfuscated, or when the project involves modernization, integration, or knowledge transfer.
Automation Tools We Can Use to Streamline Legacy System Analysis
- Dependency mappers to identify architecture components and hidden connections.
- Dynamic code analysis tools to review software runtime behavior.
- Monitoring solutions (e.g., New Relic, Dynatrace, or native logging frameworks) to analyze live system performance.
- UI analysis tools like Selenium to record and replay user interactions.
- API analysis tools like Postman to trace API communications.
- Decompilers and debuggers to convert binaries into readable formats when there’s no access to the source code.
What We Analyze to Restore Requirements
Source code with any code comments, existing documentation, and test cases
Database schemas, relationships, and stored procedures
Data flows, including data movement, processing, and transformation
Integration points, including data exchange processes and dependencies
Interfaces (APIs, GUIs) exposed by the system, including response codes in APIs
Error messages, error and exception handling mechanisms
Patterns and critical events in log files
System performance, resource usage, and health metrics
Commit history in version control systems
Configuration files and parameters
Older system versions (if available)
What Our Clients Say
Todd Nilson
MD
We worked with ScienceSoft on the refactoring and modernization of our telehealth web portal and a cross-platform patient application. We are thankful for the meticulous and value-driven approach of ScienceSoft’s team. They created comprehensive project documentation, feature lists, and worked out thorough recommendations to help us improve the stability and performance of our solution.
ScienceSoft's .NET developers have been assisting Upstream Works in refactoring a load testing system and bringing further automation to the load testing process. Their DevOps engineers helped us optimize our infrastructure and set up a continuous software delivery process. The team is very professional, well-organized, and is always on top of the finer details. This is exactly what a long-term, harmonious partnership should look like.
For the past 6 years, ScienceSoft has been a reliable partner in supporting and maintaining our HR software product. They have fixed hundreds of bugs in the product’s performance since the start of our cooperation back in 2016 and keep modernizing HR on our demand, introducing new features and tweaking existing functionality to let us better cater to our growing client base.
We commissioned ScienceSoft to audit and upgrade our partially developed AI-based software for clay pigeon shooting tracking.
ScienceSoft ramped up a development team consisting of two C++ developers, two data scientists, and a UI design expert to fulfill the project. The team identified core errors, which didn’t allow efficient solution operation, and implemented high-speed convolutional neural networks to fix them. As a result, the system could track a flying target in a real-life outdoor environment and faultlessly detect shooter’s performance.
