Skip to main content

I was bit by a 2.6 year old Fox(Pro)

Talk about old ghosts coming back to haunt you.

I was upgrading a customer's old Foxfire! installation from version 3.0 to 8.0. They were running Foxfire! 3.0 under FoxPro 2.6/Windows.

But they had this one report that kept on crashing under the 8.0 version that would run fine under their old version.

I tried to run Foxfire! 3.0 under a copy of Visual FoxPro and it crashed there as well.

Hmmm...what could it be? I looked at the SQL generated.

There was the problem that was causing the crash: one of the relationships was referring to a field that didn't even exist.

The field in the SQL referred to a field named PART_SEC_ID_I but the real field was called PART_SEC_I.

But why didn't it crash under the older version?

The reason? FoxPro 2.6 ignored the extra fields.

I opened up the table in FoxPro 2.6.

? part_sec_i
** returned a value
? part_sec_id_i
** returned the same value.

Sheesh...I can't believe we were actually able to build quality applications back then but then again, when our tool wouldn't even tell us we were wrong in our field names - who needed field validation?

Now the fun part - how to tell someone that even though their old application worked great, it was actually not reporting a problem it should have been.

Comments

Post a Comment

Popular posts from this blog

Elevating Project Specifications with Three Insightful ChatGPT Prompts

For developers and testers, ChatGPT, the freely accessible tool from OpenAI, is game-changing. If you want to learn a new programming language, ask for samples or have it convert your existing code. This can be done in Visual Studio Code (using GitHub CoPilot) or directly in the ChatGPT app or web site.  If you’re a tester, ChatGPT can write a test spec or actual test code (if you use Jest or Cypress) based on existing code, copied and pasted into the input area. But ChatGPT can be of huge value for analysts (whether system or business) who need to validate their needs. There’s often a disconnect between developers and analysts. Analysts complain that developers don’t build what they asked for or ask too many questions. Developers complain that analysts haven’t thought of obvious things. In these situations, ChatGPT can be a great intermediary. At its worst, it forces you to think about and then discount obvious issues. At best, it clarifies the needs into documented requirements. ...
Post a Comment
Read more

Respect

Respect is something humans give to each other through personal connection. It’s the bond that forms when we recognize something—or someone—as significant, relatable, or worthy of care. This connection doesn’t have to be limited to people. There was an  article  recently that described the differing attitudes towards AI tools such as ChatGPT and Google Gemini (formerly Bard). Some people treat them like a standard search while others form a sort of personal relationship — being courteous, saying “please” and “thank you”. Occasionally, people share extra details unrelated to their question, like, ‘I’m going to a wedding. What flower goes well with a tuxedo?’ Does an AI “care” how you respond to it? Of course not — it reflects the patterns it’s trained on. Yet our interaction shapes how these tools evolve, and that influence is something we should take seriously. Most of us have all expressed frustration when an AI “hallucinates”. Real or not, the larger issue is that we have hi...
Post a Comment
Read more

When A Machine Starts To Care

“Any sufficiently advanced technology is indistinguishable from magic.” Arthur C. Clarke (1962)  I first used that quote when I was starting out in the tech industry. Back then, it was a way to illustrate just how fast and powerful computers had become. Querying large datasets in seconds felt magical—at least to those who didn’t build them.  Today, we’re facing something even more extraordinary. Large Language Models (LLMs) can now carry on conversations that approach human-level fluency. Clarke’s quote applies again. And just as importantly, many researchers argue that LLMs meet—or at least brush up against—the criteria of the Turing Test.  We tend to criticize LLMs for their “hallucinations,” their sometimes-confident inaccuracies. But let’s be honest: we also complain when our friends misremember facts or recount things inaccurately. This doesn’t excuse LLMs—it simply highlights that the behavior isn’t entirely alien. In some ways, it mirrors our own cognitive limits....
Post a Comment
Read more