Recent development in NL2CODE (Natural Language to Code) research allows end-users, especially novice programmers to create a concrete implementation of their ideas such as data visualization by providing natural language (NL) instructions. An NL2CODE system often fails to achieve its goal due to three major challenges: the user's words have contextual semantics, the user may not include all details needed for code generation, and the system results are imperfect and require further refinement. To address the aforementioned three challenges for NL to Visualization, we propose a new approach and its supporting tool named NL2VIZ with three salient features: (1) leveraging not only the user's NL input but also the data and program context that the NL query is upon, (2) using hard/soft constraints to reflect different confidence levels in the constraints retrieved from the user input and data/program context, and (3) providing support for result refinement and reuse.

For large-scale distributed systems, it is crucial to efficiently diagnose the root causes of incidents to maintain high system availability. The recent development of microservice architecture brings three major challenges (i.e., complexities of operation, system scale, and monitoring) to root cause analysis (RCA) in industrial settings. To tackle these challenges, in this paper, we present Groot, an event-graph-based approach for RCA. Groot constructs a real-time causality graph based on events that summarize various types of metrics, logs, and activities in the system under analysis. Moreover, to incorporate domain knowledge from site reliability engineering (SRE) engineers, Groot can be customized with user-defined events and domain-specific rules. Currently, Groot supports RCA among 5,000 real production services and is actively used by the SRE teams in eBay, a global e-commerce system serving more than 159 million active buyers per year. Over 15 months, we collect a data set containing labeled root causes of 952 real production incidents for evaluation. The evaluation results show that Groot is able to achieve 95% top-3 accuracy and 78% top-1 accuracy. To share our experience in deploying and adopting RCA in industrial settings, we conduct a survey to show that users of Groot find it helpful and easy to use. We also share the lessons learned from deploying and adopting Groot to solve RCA problems in production environments.

Program input grammars (i.e., grammars encoding the language of valid program inputs) facilitate a wide range of applications in software engineering such as symbolic execution and delta debugging. Grammars synthesized by existing approaches can cover only a small part of the valid input space mainly due to unanalyzable code (e.g., native code) in programs and lacking high-quality and high-variety seed inputs. To address these challenges, we present REINAM, a reinforcement-learning approach for synthesizing probabilistic context-free program input grammars without any seed inputs. REINAM uses an industrial symbolic execution engine to generate an initial set of inputs for the given target program, and then uses an iterative process of grammar generalization to proactively generate additional inputs to infer grammars generalized from these initial seed inputs. To efficiently search for target generalizations in a huge search space of candidate generalization operators, REINAM includes a novel formulation of the search problem as a reinforcement learning problem. Our evaluation on eleven real-world benchmarks shows that REINAM outperforms an existing state-of-the-art approach on precision and recall of synthesized grammars, and fuzz testing based on REINAM substantially increases the coverage of the space of valid inputs. REINAM is able to synthesize a grammar covering the entire valid input space for some benchmarks without decreasing the accuracy of the grammar.

Dynamic Symbolic Execution (DSE) is among the most effective techniques for structural test generation, i.e., test generation to achieve high structural coverage. Despite its recent success, DSE still suffers from various problems such as the boundary problem when applied on various programs in practice. To assist problem diagnosis for structural test generation, in this paper, we propose a visualization approach named PexViz. Our approach helps the tool users better understand and diagnose the encountered problems by reducing the large search space for problem root causes by aggregating information gathered through DSE exploration.

Many tools directly change programs, such as bug-fixing tools, program migration tools, etc. We call them program-modification tools. On the other hand, many programming languages use the C preprocessor, such as C, C++, and Objective-C. Because of the complexity of preprocessors, many program-modification tools either fail to produce sound results under the presence of preprocessor directives, or give up completely and deal only with preprocessed code.

Mobile applications, or apps for short, are gaining popularity. The input sources (e.g., touchscreen, sensors, transmitters) of the smart devices that host these apps enable the apps to offer a rich experience to the users, but these input sources pose testing complications to the developers (e.g., writing tests to accurately utilize multiple input sources together and be able to replay such tests at a later time). To alleviate these complications, researchers and practitioners in recent years have developed a variety of record-and-replay tools to support the testing expressiveness of smart devices. These tools allow developers to easily record and automate the replay of complicated usage scenarios of their app. Due to Android's large share of the smart-device market, numerous record-and-replay tools have been developed using a variety of techniques to test Android apps. To better understand the strengths and weaknesses of these tools, we present a comparison of popular record-and-replay tools from researchers and practitioners, by applying these tools to test three popular industrial apps downloaded from the Google Play store. Our comparison is based on three main metrics: (1) ability to reproduce common usage scenarios, (2) space overhead of traces created by the tools, and (3) robustness of traces created by the tools (when being replayed on devices with different resolutions). The results from our comparison show which record-and-replay tools may be the best for developers and identify future directions for improving these tools to better address testing complications of smart devices.

Power management is becoming very important in data centers. Cloud computing is also one of the newest promising data center techniques which is appealing to many big companies. As cloud computing is different from current data centers in terms of power management due to a dynamic structure and property for its online service. Power budgeting, in terms of its important role in power management, provides powerful solutions for cloud computing with dynamic capabilities. To be specific, existing methods for data centers are based on power distribution units (PDU) divided by fixed locations on physical levels. However, it is not suitable for cloud with the dynamic property. We propose a power management design based at the logical level which uses a distribution tree with classified power capping by different service or workload types. By setting multiple trees, we can differentiate and analyze the effect of workload types and Service Level Agreements (SLAs) in terms of power characteristics.