Software Analysis

The acquisition of complex software for a public mobility provider presented the project team with unexpected challenges. With an approach that combined static analysis, agile methodology and close customer coordination, the project was successfully completed. Particularly noteworthy was the flexible response to new requirements and the development of a strong relationship of trust with the customer. The insights gained provide valuable inspiration for similar projects and offer practical advice on how to successfully handle complex projects.

Podcast episode on software analysis

In this episode, I have Sonja and Helmut as guests. They talk about taking on a complex software project for a public mobility provider, where they encountered numerous unexpected problems. Using a combination of static analysis, agile methodology and close customer coordination, they were able to successfully complete the project. Particularly impressive was their ability to respond flexibly to new challenges and gain the client’s trust. Their key findings and advice provide valuable insights for anyone facing similar projects.

“We then simply provided an expert and a developer in advance, so to speak, to take a big, rough look and simply check whether the thing was worth taking over or not.” - Helmut Nitsch, Sonja Trimmel

Helmut Nitsch has been Head of Digital Assurance at Eviden since 2022 and has more than 20 years of experience in software testing. He started his career as a project manager and head of the billing team at UPC Austria, where he also led the user acceptance test team for more than seven years. He also built up the test organization at Austria’s largest telecommunications provider (A1) and did the same at Austrian Post.

Sonja Trimmel is an experienced project manager and scrum master with a solid technical background in computer science. Over the past nine years at Atos/Eviden, she has successfully managed numerous projects, demonstrating her ability to master complex technical challenges and deliver innovative solutions. Her academic background in computer science and data science has contributed significantly to her success.

Highlights of the Episode

• Automated tools for code analysis help to assess the quality, maintainability and security of software.
• It was found that a third of the code was missing or was only available in the form of compiled black boxes.
• Close collaboration and regular communication with the customer were crucial to the success of the project.
• An agile mindset and flexibility proved to be key success factors.
• Migration rehearsals were carried out to ensure a smooth process, especially as the migration took place during the vacation period.

Software analysis and migration: Success strategies from the field

The first impression counts

A successful software project begins with a clear analysis of the current situation. In an example project, which included a ticket store for local public transport, the software was introduced without detailed knowledge of the code base that had been adopted. It was therefore necessary to first gain a rough overview of the system. By providing experts to analyze the code base, it was possible to determine whether the system was at all viable and maintainable. There were numerous surprises and ambiguities in the code, which made the start of the project more difficult.

Automated analyses as the key to success

Automated tools such as SonarQube played a central role in analyzing the code quality. Static analyses provided valuable insights into the maintainability, robustness and security of the system within a very short time. These tools identified critical weaknesses and provided concrete suggestions for improvement. This made it possible to carry out a well-founded evaluation of the system even under high time pressure.

The challenge of missing documentation

Insufficient or non-existent documentation was one of the biggest hurdles in the project. Some system components were delivered late or were only available as black boxes. This made it considerably more difficult to understand the entire system. However, these hurdles were overcome through close cooperation and clear communication. The team’s flexibility and adaptability proved to be decisive factors here.

Agile mindset as a success factor

An agile mindset was essential in order to be able to react quickly to unexpected challenges. Flexibility was required from both the customer and the team. This attitude made it easier to find solutions to problems that suddenly arose and to successfully complete the project despite numerous surprises. The client’s willingness to actively participate and apply agile methods also contributed significantly to the success.

Top learnings for future projects

A diversified team setup is a key factor for successful projects. Even if a project seems easy to plan at first glance, there should always be room for adjustments. In addition, testing should never be neglected. A comprehensive testing strategy saves time and money in the long term, as it helps to identify and eliminate weaknesses at an early stage. These findings are essential for the successful implementation of future projects.

Frequently asked questions about software analysis

What are the most common challenges in software analysis and how can they be overcome?

The most common challenges in software analysis are unclear requirements, communication problems and technological complexity. Unclear requirements can be improved through regular meetings with stakeholders and clearly documented goals. Communication problems can be minimized through the use of collaboration tools and transparent processes. Technological complexity can be managed through targeted training and the use of proven tools and frameworks. Effective software analysis therefore requires a structured approach and open communication between all stakeholders.

How can software analysis be effectively integrated into the software development process?

To effectively integrate software analysis into the software development process, it should start early, ideally in the planning phase. Regular analyses throughout development help to clearly define requirements and identify potential problems at an early stage. Feedback loops with users are also important in order to continuously improve the software. The use of tools for statistical analysis and code reviews also supports the process. These measures make software analysis an integral component that increases quality and efficiency.

Which tools are best suited for software analysis?

Tools such as SonarQube, Visual Studio and Eclipse are particularly suitable for software analysis. SonarQube helps to identify code quality and security problems. Visual Studio offers integrated analysis functions to improve the code. Eclipse has numerous plugins that support software analysis. These tools make it easier to detect errors and optimize code, resulting in better software development. They are useful for both individual developers and teams.

How does software analysis improve quality assurance in software development?

Software analysis improves quality assurance in software development by identifying errors at an early stage and uncovering systematic problems. The thorough examination of requirements, design and code ensures that the software meets expectations. This analysis also promotes clear documentation and communication within the team, which minimizes misunderstandings. Ultimately, software analysis leads to more stable and reliable products that better meet the needs of users.

How does modeling contribute to improving software analysis?

Modeling improves software analysis by visually representing complex systems, which promotes understanding and communication. Through diagrams and models, relationships and processes can be recognized more clearly, making errors and inconsistencies visible earlier. Modeling also enables the simulation of scenarios to proactively identify potential problems. Overall, it ensures that software analysis is more targeted, efficient and error-free.

How do use cases contribute to improving software analysis?

Use cases improve software analysis by presenting clear scenarios for user interaction. They help to concretize requirements in an understandable way and ensure a focus on the most important functions. By visualizing processes and user needs, use cases make it easier to identify errors and potential for improvement in the software. They also promote communication between developers and stakeholders, which leads to better implementation of the desired functions. In software analysis, they are therefore a central tool for increasing the quality and user-friendliness of the software.

Why does requirements analysis play a crucial role in software analysis?

Requirements analysis is crucial for software analysis as it ensures that the solutions developed meet the actual needs of the users. By precisely identifying and documenting requirements, misunderstandings and costly changes in later phases can be avoided. It also forms the basis for the design, implementation and testing of the software, ensuring the quality and functionality of the final product. An effective requirements process also promotes communication between stakeholders and developers.

What are the main differences between an ACTUAL analysis and a TARGET analysis?

The main differences between ACTUAL analysis and TARGET analysis in software analysis lie in the focus: ACTUAL analysis examines the current state of a system, its weaknesses and problems. In contrast, the TARGET analysis defines the desired goals and requirements for the future. While the ACTUAL analysis provides an inventory, the TARGET analysis lays the foundation for improvements and new developments. Both methods are essential for successful software projects, as they provide a clear basis for planning and implementation.

Which methods of software analysis are most effective for software development?

The most effective methods of software analysis in software development are static and dynamic analysis. Static analysis checks the source code for errors before the software is executed, while dynamic analysis evaluates the behavior of the software during execution. Both methods help to identify security gaps and bugs at an early stage, which increases the quality of the software. In addition, requirements analysis and user feedback are essential in order to tailor the software precisely to the needs of the users. Together, these approaches improve the software analysis and promote successful development.

What is software analysis and what methods are there?

Software analysis is the process of gathering information about a software system in order to understand its requirements, structure and functionality. Software analysis methods include functional analysis, data flow analysis, object-oriented analysis and UML modeling. These methods help to document the software, identify errors and recognize potential for improvement. A well-founded software analysis is crucial for the quality and success of software projects.