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.

With the steady advances in tooling to support software engineering, mastering all the features of modern IDEs, version control systems, and project trackers is becoming increasingly difficult. Answering even the most common developer questions can be surprisingly tedious and difficult. In this article we present a user study with 35 subjects to evaluate our quasi-natural language interface that provides access to various facets of the evolution of a software system but requires almost zero learning effort. Our approach is tightly woven into the Eclipse IDE and allows developers to answer questions related to source code, development history, or bug and issue management. The results of our evaluation show that our query interface can outperform classical software engineering tools in terms of correctness, while yielding significant time savings to its users and greatly advancing the state of the art in terms of usability and learnability.

The feature list of modern IDEs is steadily growing and mastering these tools becomes more and more demanding, especially for novice programmers. Despite their remarkable capabilities, IDEs often still cannot directly answer the questions that arise during program comprehension tasks. Instead developers have to map their questions to multiple concrete queries that can be answered only by combining several tools and examining the output of each of them manually to distill an appropriate answer. Existing approaches have in common that they are either limited to a set of predefined, hardcoded questions, or that they require to learn a specific query language only suitable for that limited purpose. We present a framework to query for information about a software system using guided-input natural language resembling plain English. For that, we model data extracted by classical software analysis tools with an OWL ontology and use knowledge processing technologies from the Semantic Web to query it. We use a case study to demonstrate how our framework can be used to answer queries about static source code information for program comprehension purposes.

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.