The management of cloud service incidents (unplanned interruptions or outages of a service/product) greatly affects customer satisfaction and business revenue. After years of efforts, cloud enterprises are able to solve most incidents automatically and timely. However, in practice, we still observe critical service incidents that occurred in an unexpected manner and orchestrated diagnosis workflow failed to mitigate them. In order to accelerate the understanding of unprecedented incidents and provide actionable recommendations, modern incident management system employs the strategy of AIOps (Artificial Intelligence for IT Operations). In this paper, to provide a broad view of industrial incident management and understand the modern incident management system, we conduct a comprehensive empirical study spanning over two years of incident management practices at Microsoft. Particularly, we identify two critical challenges (namely, incomplete service/resource dependencies and imprecise resource health assessment) and investigate the underlying reasons from the perspective of cloud system design and operations. We also present IcM BRAIN, our AIOps framework towards intelligent incident management, and show its practical benefits conveyed to the cloud services of Microsoft.

Towards intelligent incident management: why we need it and how we make it

Classification of weed using machine learning techniques: a review—challenges, current and future potential techniques

A Decade of Code Comment Quality Assessment: A Systematic Literature Review

Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE) problems and tasks. However, since the renaissance of DL techniques is still very recent, we lack works that summarize and condense the most recent and relevant research conducted at the intersection of DL and SE. Therefore, in this paper, we describe the first results of a mapping study covering 81 papers about DL & SE. Our results confirm that DL is gaining momentum among SE researchers over the years and that the top-3 research problems tackled by the analyzed papers are documentation, defect prediction, and testing.

Software engineering meets deep learning: a mapping study

Software Engineering Meets Deep Learning: A Mapping Study.

A domain glossary that organizes domain-specific concepts and their aliases and relations is essential for knowledge acquisition and software development. Existing approaches use linguistic heuristics or term-frequency-based statistics to identify domain specific terms from software documentation, and thus the accuracy is often low. In this paper, we propose a learning-based approach for automatic construction of domain glossary from source code and software documentation. The approach uses a set of high-quality seed terms identified from code identifiers and natural language concept definitions to train a domain-specific prediction model to recognize glossary terms based on the lexical and semantic context of the sentences mentioning domain-specific concepts. It then merges the aliases of the same concepts to their canonical names, selects a set of explanation sentences for each concept, and identifies "is a", "has a", and "related to" relations between the concepts. We apply our approach to deep learning domain and Hadoop domain and harvest 5,382 and 2,069 concepts together with 16,962 and 6,815 relations respectively. Our evaluation validates the accuracy of the extracted domain glossary and its usefulness for the fusion and acquisition of knowledge from different documents of different projects.

A learning-based approach for automatic construction of domain glossary from source code and documentation

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A learning-based approach for automatic construction of domain glossary from source code and documentation