Reading a programming error message is the first step in understanding what it is trying to tell the programmer about how to fix an error in their code. However, these are often difficult to read, especially for novices which is not surprising given that error messages in many of the most popular languages in which novices learn to code were not written with readability in mind. As a result, novices frequently struggle to understand them. This is a long-standing problem, with researchers highlighting concerns about programming error message readability over the last six decades. Very recent work has put forward evidence of the need for measuring readability in error messages and a framework for doing so. This framework consists of four factors of readability for programming error messages: message length, vocabulary, jargon, and sentence construction. We use this framework to implement an approach to automatically assess the readability of programming error messages. Using established readability factors as predictors in a machine learning model, we train several models using a dataset of C and Java error messages. We examine the performance of these models, and apply the best performing model to a previously published set of messages evaluated for readability by experts, non-experts and students. Our results validate the previously proposed readability factors, and our model classifies messages similarly to human raters. Finally, we discuss future work needed to improve the accuracy of the model.

The amount of computing done in the cloud is greatly increasing. The decentralized nature of the cloud, however, makes it difficult for individuals to ensure that the computation is being done correctly. Thus, the concept of "cloud auditing" has appeared. As applications in the cloud become more sensitive, the need for auditing systems to provide rapid analysis and quick responses also increases. Machine learning algorithms can be employed for the purposes of providing audit data. Few of these algorithms can be done in an online fashion, however. In this work, we examine one such online machine learning algorithm, and describe how it might be employed in a distributed computing environment.

In this paper, we propose game-theoretic mechanisms to encourage truthful data sharing for distributed data mining. One proposed mechanism uses the classic Vickrey-Clarke-Groves (VCG) mechanism, and the other relies on the Shapley value. Neither relies on the ability to verify the data of the parties participating in the distributed data mining protocol. Instead, we incentivize truth telling based solely on the data mining result. This is especially useful for situations where privacy concerns prevent verification of the data. Under reasonable assumptions, we prove that these mechanisms are incentive compatible for distributed data mining. In addition, through extensive experimentation, we show that they are applicable in practice.