In the early development of source code change analysis, methodologies primarily relied on simple textual differencing, which treated code as mere text and identified changes through lines that were added, modified, or deleted. This approach overlooked the rich semantic information embedded within the code, highlighting significant limitations in textual analysis and differencing that required a more precise and language-aware foundation. Our research on ChangeDistiller pioneered the use of abstract syntax trees and associated tree edits for change analysis. We were among the first to introduce a tree-differencing algorithm for source code, enabling a fine-grained examination of modifications. ChangeDistiller has since been widely adopted by researchers in the field of mining software repositories. This paper reflects on the evolution of our technique, its influence on subsequent research, and its role in the advancement of change analysis methodologies. In addition, we explore how contemporary techniques and tools can draw on our foundational work to enhance their effectiveness.

Issue tracking repositories contain a wealth of information for reasoning about various aspects of software development processes. In this paper, we focus on bug triaging and provide visual means to explore the effort estimation quality and the bug life-cycle of reported problems. Our approach follows the micro/macro reading technique and uses a combination of graphical views to investigate details of individual problem reports while maintaining the context provided by the surrounding data population. This enables the detection and detailed analysis of hidden patterns and facilitates the analysis of problem report outliers. In an industrial study, we use our approach in various problem report analysis scenarios and answer questions related to effort estimation and resource planning.

The reasons why software is changed are manyfold; new features are added, bugs have to be fixed, or the consistency of coding rules has to be re-established. Since there are many types of of source code changes we want to explore whether they appear frequently together in time and whether they describe specific development activities. We describe a semi-automated approach to discover patterns of such change types using agglomerative hierarchical clustering. We extracted source code changes of one commercial and two open-source software systems and applied the clustering. We found that change type patterns do describe development activities and affect the control flow, the exception flow, or change the API.

A key issue in software evolution analysis is the identification of particular changes that occur across several versions of a program. We present change distilling, a tree differencing algorithm for fine-grained source code change extraction. For that, we have improved the existing algorithm of Chawathe et al. for extracting changes in hierarchically structured data. Our algorithm detects changes by finding a match between nodes of the compared two abstract syntax trees and a minimum edit script. We can identify change types between program versions according to our taxonomy of source code changes. We evaluated our change distilling algorithm with a benchmark we developed that consists of 1,064 manually classified changes in 219 revisions from three different open source projects. We achieved significant improvements in extracting types of source code changes: our algorithm approximates the minimum edit script by 45% better than the original change extraction approach by Chawathe et al. We are able to find all occurring changes and almost reach the minimum conforming edit script, i.e., we reach a mean absolute percentage error of 34%, compared to 79% reached by the original algorithm. The paper describes both the change distilling and the results of our evaluation.

Current change history analysis approaches rely on information provided by versioning systems such as CVS. Therefore, changes are not related to particular source code entities such as classes or methods but rather to text lines added and/or removed. For analyzing whether some change coupling between source code entities is significant or only minor textual adjustments have been checked in, it is essential to reflect the changes to the source code entities. We have developed an approach for analyzing and classifying change types based on code revisions. We can differentiate between several types of changes on the method or class level and assess their significance in terms of the impact of the change types on other source code entities and whether a change may be functionality-modifying or functionality-preserving. We applied our change taxonomy to a case study and found out that in many cases large numbers of lines added and/or deleted are not accompanied by significant changes but small textual adaptations (such as indentation, etc.). Furthermore, our approach allows us to relate all change couplings to the significance of the identified change types. As a result, change couplings between code entities can be qualified and less relevant couplings can be filtered out