Background: Humor is a fundamental part of human communication, with prior work linking positive humor in the workplace to positive outcomes, such as improved performance and job satisfaction. However, prior research within the software engineering context has largely overlooked the role of humor among programmers. Aims: The aim of this study was to investigate programming related humor in a large social media community. Specifically, we were interested in what software developers find humorous, and whether we can predict humor. Methodology: We collected 139,718 submission from Reddit subreddit r/ProgrammerHumor. Both textual and image-based (memes) submissions were considered. The image data was processed with OCR to extract text from images for NLP analysis. Multiple regression models were built to investigate what makes submissions humorous. Additionally, a subset of 800 submissions were labeled by human annotators regarding their relation to theories of humor, suitability for the workplace, the need of programming knowledge for understanding the submission, and lastly, whether images in image-based submissions added context to the submission. Results: Overall, our results indicate that what predicting what is found to funny be developers is difficult. Our best regression model was able to explain only 10% of the variance. However, statistically significant differences were observed between topics, times submissions are created, and associated humor theories. Our analysis reveals that the highest submission scores are achieved by image-based submissions that are created on winter months of northern hemisphere, between 2-3pm UTC on weekends, and which are distinctively related to superiority and incongruity theories of humor. Conclusions: Predicting humor with natural language processing methods is difficult. We discuss the benefits and inherent difficulties in assessing perceived humor of submissions, as well as possible avenues for future work. Upon the publication of this paper we release our dataset that can be used in future studies of programmer humor.