Bug Triaging with High Confidence Predictions

Correctly assigning bugs to the right developer or team, i.e., bug triaging, is a costly activity. A concerted effort at Ericsson has been done to adopt automated bug triaging to reduce development costs. We also perform a case study on Eclipse bug reports. In this work, we replicate the research approaches that have been widely used in the literature including FixerCache. We apply them on over 10k bug reports for 9 large products at Ericsson and 2 large Eclipse products containing 21 components. We find that a logistic regression classifier including simple textual and categorical attributes of the bug reports has the highest accuracy of 79.00% and 46% on Ericsson and Eclipse bug reports respectively. Ericsson’s bug reports often contain logs that have crash dumps and alarms. We add this information to the bug triage models. We find that this information does not improve the accuracy of bug triaging in Ericsson’s context. Eclipse bug reports contain the stack traces that we add to the bug triaging model. Stack traces are only present in 8% of bug reports and do not improve the triage accuracy. Although our models perform as well as the best ones reported in the literature, a criticism of bug triaging at Ericsson is that accuracy is not sufficient for regular use. We develop a novel approach that only triages bugs when the model has high confidence in the triage prediction. We find that we improve the accuracy to 90% at Ericsson and 70% at Eclipse, but we can make predictions for 62% and 25% of the total Ericsson and Eclipse bug reports,respectively

The Design and Implementation of Low-Latency Prediction Serving Systems

Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application. However, most machine learning frameworks and systems are optimized for model training and not deployment.In this thesis, I discuss three prediction serving systems designed to meet the needs of modern interactive machine learning applications. The key idea in this work is to utilize a decoupled, layered design that interposes systems on top of training frameworks to build low-latency, scalable serving systems. Velox introduced this decoupled architecture to enable fast online learning and model personalization in response to feedback. Clipper generalized this system architecture to be framework-agnostic and introduced a set of optimizations to reduce and bound prediction latency and improve prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. And InferLine provisions and manages the individual stages of prediction pipelines to minimize cost while meeting end-to-end tail latency constraints

Source Code Retrieval from Large Software Libraries for Automatic Bug Localization

This dissertation advances the state-of-the-art in information retrieval (IR) based approaches to automatic bug localization in software. In an IR-based approach, one first creates a search engine using a probabilistic or a deterministic model for the files in a software library. Subsequently, a bug report is treated as a query to the search engine for retrieving the files relevant to the bug. With regard to the new work presented, we first demonstrate the importance of taking version histories of the files into account for achieving significant improvements in the precision with which the files related to a bug are located. This is motivated by the realization that the files that have not changed in a long time are likely to have ``stabilized and are therefore less likely to contain bugs. Subsequently, we look at the difficulties created by the fact that developers frequently use abbreviations and concatenations that are not likely to be familiar to someone trying to locate the files related to a bug. We show how an initial query can be automatically reformulated to include the relevant actual terms in the files by an analysis of the files retrieved in response to the original query for terms that are proximal to the original query terms. The last part of this dissertation generalizes our term-proximity based work by using Markov Random Fields (MRF) to model the inter-term dependencies in a query vis-a-vis the files. Our MRF work redresses one of the major defects of the most commonly used modeling approaches in IR, which is the loss of all inter-term relationships in the documents

Computer Aided Verification

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic

Predictive analysis of real-time strategy games using graph mining

Machine learning and computational intelligence have facilitated the development of recommendation systems for a broad range of domains. Such recommendations are based on contextual information that is explicitly provided or pervasively collected. Recommendation systems often improve decision-making or increase the efficacy of a task. Real-Time Strategy (RTS) video games are not only a popular entertainment medium, they also are an abstraction of many real-world applications where the aim is to increase your resources and decrease those of your opponent. Using predictive analytics, which examines past examples of success and failure, we can learn how to predict positive outcomes for such scenarios. To do this, one way to represent this type of data in order to model relationships between entities is by using graphs. The vast amount of data has resulting in complex and large graphs that are difficult to process. Hence, researchers frequently employ parallelized or distributed processing. But first, the graph data must be partitioned and assigned to multiple processors in such a way that the workload will be balanced, and inter-processor communication will be minimized. The latter problem may be complicated by the existence of edges between vertices in a graph that have been assigned to different processors. One objective of this research is to develop an accurate predictive recommendation system for multiplayer strategic games to determine recommendations for moves that a player should, and should not, make which can provide a competitive advantage. Another objective is to determine how to partition a single undirected graph in order to optimize multiprocessor load balancing and reduce the number of edges between split subgraphs --Abstract, page iv

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

New forms of collaborative innovation and production on the internet

The Internet has enabled new forms of large-scale collaboration. Voluntary contributions by large numbers of users and co-producers lead to new forms of production and innovation, as seen in Wikipedia, open source software development, in social networks or on user-generated content platforms as well as in many firm-driven Web 2.0 services. Large-scale collaboration on the Internet is an intriguing phenomenon for scholarly debate because it challenges well established insights into the governance of economic action, the sources of innovation, the possibilities of collective action and the social, legal and technical preconditions for successful collaboration. Although contributions to the debate from various disciplines and fine-grained empirical studies already exist, there still is a lack of an interdisciplinary approach