Saturday, March 18, 2023

More LintGPT

My earlier post about using ChatGPT to help identify Perl program bugs was only part of the story. I had a second bug that also took me way too long to identify (I was sleep-deprived; so sue me). I had high hopes that Chat would find it quickly:


Given the probable intention of the programmer, what is wrong with this perl code?

foreach my $k (%stats) {

  if ($k =~ /rx.*packet|rx.*_to_|drop/) {

    my $diff = $stats{$k} - $prev_stats{$k};

    if ($diff > 0) {

      print "    ???$k increased by $diff\n";





Based on the code snippet provided, it seems to be scanning through the %stats hash and checking if the keys match a certain regular expression pattern. If there is a match, it calculates the difference between the current value of the key and the previous value stored in %prev_stats. If the difference is greater than zero, it prints a message indicating that the value has increased.

However, there are some potential issues with this code:

1. The regular expression used to match the keys (/rx.*packet|rx.*_to_|drop/) may not match all the keys that it is intended to match. For example, it will not match keys that contain "rx" followed by something other than "packet" or "to", or keys that contain "dropped" instead of "drop". Depending on the data being processed, this could result in missed updates or false positives.

2. The code assumes that all keys in %stats and %prev_stats are numeric values that can be subtracted from each other. If any of the values are non-numeric (e.g., strings), this could result in errors or unexpected behavior.

3. The code does not check if a key exists in %prev_stats before calculating the difference. If a key exists in %stats but not in %prev_stats, this could result in an error.

4. The message printed by the code ("???$k increased by $diff\n") is not very informative and may be difficult to understand without additional context. It may be helpful to provide more detailed information about what the key represents and why its increase is significant.


You correctly identified the intent of scanning through the %stats hash and checking keys against a regular expression. Does the fragment "foreach my $k (%stats) {" correctly scan the hash?


No, the fragment "foreach my $k (%stats) {" does not correctly scan the hash.

When iterating over a hash in Perl, you need to use one of the following methods:

    Using the keys function:

foreach my $k (keys %stats) {

  # do something with $stats{$k}


This method returns an array of all the keys in the hash, which you can then iterate over.

    Using the each function:

while (my ($k, $v) = each %stats) {

  # do something with $v


This method returns each key-value pair in the hash in turn.

Using the syntax "foreach my $k (%stats)" will treat %stats as a list, with the keys and values interleaved, which is not what is intended in this code.


You are exactly correct. The programmer omitted the keys function. Why didn't you identify that in your first response?


I apologize for the oversight in my first response. You are correct that I did not identify the issue with the way the %stats hash is being scanned in the foreach loop.

As you pointed out, the correct way to iterate over the keys of a hash in Perl is to use the keys function. Using %stats as the loop variable in the foreach loop will cause Perl to interpret it as a list, with the keys and values of the hash interleaved.

I should have caught this mistake in my initial response, and I apologize for any confusion it may have caused. Thank you for bringing it to my attention.


This is interesting to me. I wrote the code, and I accidentally omitted the "keys" function. So we both had the same oversight. But since you are an AI language model, I assume the underlying reason for the oversight is very different between you and me.


That is a fascinating observation! As an AI language model, my understanding of code is based on statistical patterns learned from large amounts of training data. In this case, my response was based on recognizing the general structure of the code and identifying potential issues based on common coding practices and patterns.

However, as a machine learning model, I do not have the same ability to reason about code as a human programmer. I cannot recognize the programmer's intent, understand the context of the code, or make judgment calls based on experience or domain knowledge.

In contrast, humans can use their intuition, reasoning, and experience to catch errors and identify issues that may not be obvious from the code itself. We can also apply domain-specific knowledge and expertise to understand the implications of different coding patterns and make informed decisions.

So while AI language models like myself can be helpful in identifying common coding issues and providing suggestions, we cannot replace the creativity, insight, and problem-solving abilities of human programmers.

Chat was wrong about the statement:

... For example, it will not match keys ... that contain "dropped" instead of "drop".

Since I did not include a "$" anchor, my regexp *will* match "dropped". Chat is not actually parsing and understanding the regexp, it is just generating a hypothetical example of something that *might* not match.

But what about, "In contrast, humans can use their intuition... We can also apply domain-specific knowledge..." Whoa, it just included itself in the human group! But again, I assume that is just an artifact of its statistical model for content generation. It has seen so many instances of "we can also..." that it used it.

Sabine Hossenfelder has an interesting video about chatbots "understanding" what they are talking about. She observes that both humans and neural net-based AIs learn in many of the same ways: by detecting patterns and building models of reality; models that can be used to answer questions. Maybe a human's understanding of various topics is "better" because the mental model is more sophisticated and can answer more questions accurately, but this is just a matter of degree, not fundamental difference. I've been interested to find all kinds of cases where ChatGPT appears to have good understanding and where that understanding falls apart. But it's certainly true that my own understandings of any number of phenomena are much worse than an expert's understanding, and maybe the errors ChatGPT makes are not so different than the errors I make.

All very interesting.

P.S. - I have a lot more content about AI in my wiki.

No comments: