To perform a number of tasks such as inferring design rationale from past code changes or assessing developer expertise for a software feature or bug, the evolution of a set of lines of code can be assessed by mining software histories. However, determining the evolution of a set of lines of code is a manual and time consuming process. This paper presents a model of this process and an approach for automating it. We call this process History Slicing. We describe the process and options for generating a graph that links every line of code with its corresponding previous revision through the history of the software project. We then explain the method and options for utilizing this graph to determine the exact revisions that contain changes for the lines of interest and their exact position in each revision. Finally, we present some preliminary results which show initial evidence that our automated technique can be several orders of magnitude faster than the manual approach and require that developers examine up to two orders of magnitude less code in extracting such histories.

In my research, I propose an automated technique to support bug investigation by using a novel analysis of the history of the source code. During the bug-fixing process, developers spend a high amount of manual effort investigating the bug in order to answer a series of questions about it. My research will support developers in answering the following questions about a bug: Who is the most suitable developer to fix the bug?, Where is the bug located?, When was the bug inserted? and Why was the bug inserted?

This paper describes a new technique, which automatically selects the most appropriate developers for fixing the fault represented by a failing test case, and provides a diagnosis of where to look for the fault. This technique works by incorporating three key components: (1) fault localization to inform locations whose execution correlate with failure, (2) history mining to inform which developers edited each line of code and when, and (3) expertise assignment to map locations to developers. To our knowledge, the technique is the first to assign developers to execution failures, without the need for textual bug reports. We implement this technique in our tool, WHOSEFAULT, and describe an experiment where we utilize a large, open-source project to determine the frequency in which our tool suggests an assignment to the actual developer who fixed the fault. Our results show that 81% of the time, WHOSEFAULT produced the same developer that actually fixed the fault within the top three suggestions. We also show that our technique improved by a difference between 4% and 40% the results of a baseline technique. Finally, we explore the influence of each of the three components of our technique over its results, and compare our expertise algorithm against an existing expertise assessment technique and find that our algorithm provides greater accuracy, by up to 37%.

In this paper, we present CHRONOS-a tool that enables the querying, exploration, and discovery of historical change events to source code. Unlike traditional Revision-Control-System tools, CHRONOS allows queries across any subset of the code, down to the line-level, which can potentially be contiguous or disparate, even among multiple files. In addition, CHRONOS provides change history across all historical versions (i.e., it is not limited to a pairwise "diff"). The tool implements a zoom-able user interface as a visualization of the history of the queried code to provide both a high-level view of the changes, which supports pattern recognition and discovery, and a lowlevel view that supports semantic comprehension for tasks such as reverse engineering and identifying design rationale. In this paper, we describe use cases in which CHRONOS may be helpful, provide a motivating example to demonstrate the benefits brought by CHRONOS, and describe its visualization in detail.