Testing atoms isolated in minimal examples, like in the atoms existence experiment, is crucial for gaining a rigorous understanding of the relative confusion and removability of an individual atom. To broaden our understanding of how multiple atoms affect larger bodies of code, we tested their impact on a broader sample from the source of the code snippets. The experiment used winning programs from the IOCCC to test subjects’ ability to hand evaluate each full program. For each code sample, half of our subjects performed the hand evaluation task on code in its near-original form, and the other half evaluated the same code after all of its known atoms were removed. Our hypothesis was that programmers would make fewer evaluation errors on code which had its atoms of confusion removed.

With the data from this experiment we will explore the following questions:

  • Are clarified programs evaluated more accurately than obfuscated programs?
  • What other ways can we measure subject performance?
  • Which code was still confusing in clarified questions?

We recruited 43 programmers with at least 6 months experience with C or C++. We presented them (1) small programs (between 14-84 lines) containing several atoms of confusion, and (2) a version of the same programs transformed to remove the atoms of confusion. The subjects were asked to hand evaluate the output of both versions of the programs. We would like to verify that multiple atoms of confusion cause more errors than other code in hand-evaluated outputs.


Before doing the experiment, all subjects needed to sign a consent form. This form explains the purpose of this experiment, and informs the subject that the participation is voluntary.

Core materials


We designed instructions for in-person study, and remote study, as some of our subjects were remote.

Questions and sample answers

In total, we designed four programs, each with its confusing (with atoms) and non-confusing version (with atoms removed). The full list of programs can be found here. They are named as question A/B/C/D/E/F/G/H, respectively. For example, question A and B are the confusing and non-confusing version of the same program. For each subject, we chose a random subset of four out of the eight questions, and made sure that they do not see both the confusing and non-confusing version of the same program.

After each experiment with a subject, we scanned the hand written results, normalized and transcribed the results. Here is a sample answer from a subject after we obtained consent. The answer includes both the hand written and the transcribed versions.

We transcibed each result, available here. Note: a fully coded and normalized version is available below.


Subjects completed a demographic survey after finishing all the questions.

We have chosen not to release the data gathered this survey due to concerns about subject anonymity. If you would like to receive sanitized subsets of the data please contact us to discuss if this would be possible.

Data normalization

All user responses were hand-written and had to be manually transcribed. The format of the subject responses occassionally varied and necessitated a data normalization pass. Two researchers went over every response and corrected typos and small errors where possible and coded common patterns. Both researchers compared their suggested changes with each other, and only changes agreed upon by both coders were implemented. These are the types of changes that were made:

  • If the subject added superfluous commas, quotes, control characters, etc we removed them.
  • If the subject wrote a parenthetical comment we removed the comment.
  • If the subject left out a value, or wrote “value in memory”, “random address”, etc we replaced it with ?.
  • If the subject wrote “error”/”segfault”/”infinite loop” we replaced it with X.
  • If the subject wrote “I gave up” we replaced it with !.

The normalized data set is available here. Note: a raw, uncoded/unnormalized version is available above.


Transcribed results were graded, aggregated, and formatted with programs described in this README.

Statistical analysis

Data was analyzed using R code located here.


Any known issues with the study discovered after the administration and collection of data are described in detail here