Abstract Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.

Code remains largely hand-made by humans and, as such, writing code is prone to error. Many previous studies have focused on the technical reasons for these errors and provided developers with increasingly sophisticated tools. Few studies have looked in detail at why code errors have been made from a human perspective. We use Human Error Theory to frame our exploratory study and use semi-structured interviews to uncover a preliminary understanding of the errors developers make while coding. We look particularly at the Skill-based (SB) errors reported by 27 professional software developers. We found that the complexity of the development environment is one of the most frequently reported reasons for errors. Maintaining concentration and focus on a particular task also underpins many developer errors. We found that developers struggle with effective mitigation strategies for their errors, reporting strategies largely based on improving their own willpower to concentrate better on coding tasks. We discuss how using Reason’s Swiss Cheese model may help reduce errors during software development. This model ensures that layers of tool, process and management mitigation are in place to prevent developer errors from causing system failures.

Context: The vast majority of software engineering research is reported independently of the application domain: techniques and tools usage is reported without any domain context. As reported in previous research, this has not always been so: early in the computing era, the research focus was frequently application domain specific (for example, scientific and data processing).