A technology that is reliable today may become a risk tomorrow. How can organizations recognize the warning signs early enough to make informed decisions about the technologies they depend on? In this master's thesis, you will investigate which signals can indicate future software lifecycle risks and how they can support technology management decisions.

Modern software systems depend on a large number of third-party technologies, frameworks, platforms and open-source components. These technologies continuously evolve: new versions are released, support lifecycles change, communities grow or decline, and security vulnerabilities emerge. As a result, organizations must continuously decide which technologies to adopt, maintain, upgrade or replace.

Making these decisions is challenging. While many signals are available, such as support status, security vulnerabilities, release activity and ecosystem maturity, it is often unclear which factors are most relevant for assessing long-term lifecycle risk. Consequently, technology management decisions may be driven by individual events rather than a broader understanding of the risks associated with a software dependency.

The Assignment

In this assignment, you will investigate software lifecycle risks within modern software ecosystems. You will explore which characteristics of technologies, platforms, frameworks or open-source projects may indicate future lifecycle risks and how these risks can be assessed.

Possible research methods include a literature review of existing lifecycle and software health indicators, analysis of historical data from open-source projects, and interviews with experts involved in technology management. Open-source projects can provide data on signals such as development and release activity, contributor activity, security vulnerabilities and ecosystem characteristics.

Existing initiatives such as CHAOSS and OpenSSF Scorecard provide metrics for assessing aspects of project health and security. Related research has explored forecasting contributor activity , project abandonment and survival , and assessing open-source ecosystem health . These initiatives and studies provide possible starting points for investigating which signals are useful for anticipating future lifecycle risks.

Based on your research, you will develop and evaluate an approach for assessing software lifecycle risks and supporting technology management decisions. Possible outcomes include an empirically validated risk model or a prototype tool that demonstrates how such an approach could be applied in practice. The outcome should provide practical insights that help organizations make informed decisions about adopting, maintaining, upgrading or replacing software technologies.

About Info Support

Info Support specializes in custom software, data/AI solutions, management, and training and is active in the Finance, Industry, Agriculture, Food & Retail, Mobility & Public, and Healthcare sectors. We provide solid and innovative solutions for complex and critical software issues. Our headquarters are located in Veenendaal (NL) and Mechelen (BE). At present, approximately 500 employees are employed by Info Support.

Info Support's working method is characterized by a number of core values: solidity, integrity, craftsmanship, and passion. These core values are intertwined in our work and the way we interact with each other.

To ensure that all employees are always up to date with the latest developments, Info Support has an in-house IT Academy that eagerly satisfies the hunger for more or different knowledge and skills.

B2 language proficiency in Dutch is required.

Predicting software lifecycle risks in modern software...

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