We are in a coding crisis | Simon Thorne | TEDxAberystwyth | Summary and Q&A

TL;DR

Despite advancements in technology, software engineering still faces challenges in terms of quality, human error, and accountability.

Key Insights

• ☠️ Software error rates have not significantly improved over the years, indicating the persistent challenges in software engineering.
• ☠️ End user computing, particularly involving spreadsheets, exhibits alarming quality issues with high error rates.
• ❓ Poor software quality has resulted in catastrophic consequences, including wrongful convictions and fatal accidents.
• 👨‍💻 Low code and no code tools provide accessibility but increase the risk of untested, undocumented software.
• 🖤 The field of AI lacks standardized practices, necessitating the establishment of standards and validation models.
• ❓ Legislation ensuring accountability for software failures can deter vendors from releasing subpar software.
• 🥰 Software engineers should prioritize precision, embrace established wisdom, and consider programming as an art rather than a commodity.

Transcript

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Questions & Answers

Q: Why do students dislike programming?

Students' aversion to programming can stem from the inherent difficulty of concentration and precision required, as well as the perception that it is an inconvenience rather than a creative pursuit.

Q: What are the consequences of poor software quality?

Poor software quality can lead to catastrophic failures, such as wrongful convictions in the Horizon Post Office Scandal, loss of vital data in the UK test and trace system, and fatal accidents, as seen in the Airbus MCS system.

Q: How do low code and no code tools impact software engineering?

While low code and no code tools make software development more accessible, they also increase the risk of untested, undocumented applications created by non-software engineers, similar to the issues observed in spreadsheets.

Q: What solutions can address the challenges in software engineering?

To improve software engineering practices, it is essential to incorporate AI and automation artifacts into formal software engineering processes, establish standards for AI, introduce accountability through legislation, and prioritize human factors considerations.

Summary & Key Takeaways

• Software error rates have remained relatively consistent over the past few decades, with 2.5% errors per line of code and 5% in non-debug software.

• End user computing, particularly in spreadsheets, exhibits even worse quality issues, with 88% of spreadsheets containing material errors.

• Recent examples, such as the Horizon Post Office Scandal, the UK test and trace system, and the Airbus MCS system, highlight the catastrophic consequences of poor software quality and inadequate testing.