David Shepherd

Hui Chen, Kostadin Damevski, David Shepherd et al.

Traces of user interactions with a software system, captured in production, are commonly used as an input source for user experience testing. In this paper, we present an alternative use, introducing a novel approach of modeling user interaction traces enriched with another type of data gathered in production - software fault reports consisting of software exceptions and stack traces. The model described in this paper aims to improve developers' comprehension of the circumstances surrounding a specific software exception and can highlight specific user behaviors that lead to a high frequency of software faults. Modeling the combination of interaction traces and software crash reports to form an interpretable and useful model is challenging due to the complexity and variance in the combined data source. Therefore, we propose a probabilistic unsupervised learning approach, adapting the Nested Hierarchical Dirichlet Process, which is a Bayesian non-parametric topic model commonly applied to natural language data. This model infers a tree of topics, each of whom describes a set of commonly co-occurring commands and exceptions. The topic tree can be interpreted hierarchically to aid in categorizing the numerous types of exceptions and interactions. We apply the proposed approach to large scale datasets collected from the ABB RobotStudio software application, and evaluate it both numerically and with a small survey of the RobotStudio developers.

Johannes Schneider, Avi Bernstein, Jan Vom Brocke et al.

All methodologies for detecting plagiarism to date have focused on the final digital "outcome", such as a document or source code. Our novel approach takes the creation process into account using logged events collected by special software or by the macro recorders found in most office applications. We look at an author's interaction logs with the software used to create the work. Detection relies on comparing the histograms of multiple logs' command use. A work is classified as plagiarism if its log deviates too much from logs of "honestly created" works or if its log is too similar to another log. The technique supports the detection of plagiarism for digital outcomes that stem from \emph{unique} tasks, such as theses and \emph{equal} tasks such as assignments for which the same problem sets are solved by multiple students. Focusing on the latter case, we evaluate this approach using logs collected by an interactive development environment (IDE) from more than sixty students who completed three programming assignments.

Kostadin Damevski, David Shepherd, Nicholas Kraft et al.

This is position paper accepted to the Computational Science & Engineering Software Sustainability and Productivity Challenges (CSESSP Challenges) Workshop, sponsored by the Networking and Information Technology Research and Development (NITRD) Software Design and Productivity (SDP) Coordinating Group, held October 15th-16th 2015 in Washington DC, USA. It discusses the role recommendation systems, based on textual and structural information in source code, and further enhanced by mining related applications, can have in improving the portability of scientific and engineering software.

Xi Ge, David Shepherd, Kostadin Damevski et al.

Developers spend a significant amount of time searching their local codebase. To help them search efficiently, researchers have proposed novel tools that apply state-of-the-art information retrieval algorithms to retrieve relevant code snippets from the local codebase. However, these tools still rely on the developer to craft an effective query, which requires that the developer is familiar with the terms contained in the related code snippets. Our empirical data from a state-of-the-art local code search tool, called Sando, suggests that developers are sometimes unacquainted with their local codebase. In order to bridge the gap between developers and their ever-increasing local codebase, in this paper we demonstrate the recommendation techniques integrated in Sando.