Case Study on Software Requirements Specification

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I recently recorded a lecture on Software Requirements Specifications (SRS), using my project Cloud-Based Multi-Service Platform for Smart Event Management as a case study. In the video, I walk through: Functional & non-functional requirements Technical requirements Security considerations Testing strategy System architecture Video: https://www.youtube.com/watch?v=C4tE1kZNrX4 Full SRS: https://github.com/UlyssesAlves/case-study-smart-event-manag... Article: https://zoepsomi.com/en/technical-art-software-requirements-... My goal is to show how an SRS looks in practice and why it matters for real-world projects. I’d love feedback from the community on how you approach writing and applying SRS in your own work.

Visit product View on Hacker NewsNov 20, 2025

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: platform · Missing: plus, intuitive, reviews

56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

40%40% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Strong signals: using · Missing: mac, agents, macos

17%17% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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