A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits

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.Comment: Status: Accepted at Empirical Software Engineerin

Unlocking success in process mining adoption : a comprehensive exploration of human resources and team configuration

Process mining, while rooted in technical intricacies, is greatly influenced by the human component that drives its adoption. Moving beyond the purely technical aspects of process mining, my PhD research emphasizes the often-overlooked human element in process mining adoption, shedding light on the intricate interplay between technology and the people who wield it. This paper outlines the research project that delves into the dynamics of team configurations, seeking to understand and optimize them for more effective process mining implementation. Adopting a mixed-method approach, my PhD research intricately weaves quantitative data with qualitative insights, ensuring a comprehensive understanding of the subject. My study underscores the significance of a people-centric approach, advocating that the success of process mining projects hinges not just on the technology itself, but also on the competencies, role, and configurations of teams behind it. Through this lens, this project offer organizations a roadmap to seamlessly integrate process mining into their operations, ensuring both technological prowess and human expertise are harmoniously aligned

Uncovering the Combined Impact of Process Characteristics and Reward Types on Employees’ Job Satisfaction: A European Quantitative Study

Organizations should constantly improve their business processes to increase performance while keeping employees satisfied. Therefore, human actors are considered a success factor in business process management (BPM) projects. This fact amplifies the impact of employees’ satisfaction on business process performance. Although several reward approaches exist, it remains unclear how they affect job satisfaction specifically in combination with certain process characteristics. To address this gap, we conducted a statistical analysis of survey data based on a representative European working conditions dataset. We applied two-way analysis of variance (ANOVA) and analysis of covariance (ANCOVA, i.e., controlled for organization size and sector) to explore the interaction effects. By looking at all possible combinations, we uncover how the presence or absence of specific pay modes and process-related aspects influence job satisfaction. Additionally, we reveal and discuss the joint effect of process characteristics and pay-for-performance types on job satisfaction. The results argue for a differentiated approach in pay-for-performance to obtain optimal reward solutions. Moreover, we advise for better strategic planning and facilitating successful BPM implementation. </jats:p

On current job market demands for process mining : a descriptive analysis of LinkedIn vacancies

Process mining is growing to a billion-dollar market, focusing on dedicated techniques for improving existing business processes. With the increasing popularity and application of process mining, most scholars have focused on technical research while the organizational and people-related aspects remain underinvestigated. To partly fill the gap, this paper explores current job market demands in process mining by means of an empirical and analytical study of vacancies on LinkedIn platform. Our dataset uncovers a wide variety of vacancies from 47 countries, including organizations of different sizes and 12 sectors. The vacancies are issued by end-user companies, vendors or consultancy firms and include a combination of technical or business orientation. Given this wide variety among process mining vacancies, future research can also benefit from better complying with companies’ needs

A fine-grained data set and analysis of tangling in bug fixing commits

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. Objectives: 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. Conclusions: 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