Facilitating software evolution through natural language comments and dialogue

Software projects are continually evolving, as developers incorporate changes to refactor code, support new functionality, and fix bugs. To uphold software quality amidst constant changes and also facilitate prompt implementation of critical changes, it is desirable to have automated tools for supporting and driving software evolution. In this thesis, we explore tasks and data and design machine learning approaches which leverage natural language to serve this purpose. When developers make code changes, they sometimes fail to update the accompanying natural language comments documenting various aspects of the code, which can lead to confusion and vulnerability to bugs. We present our work on alerting developers of inconsistent comments upon code changes and suggesting updates by learning to correlate comments and code. When a bug is reported, developers engage in a dialogue to collaboratively understand it and ultimately resolve it. While the solution is likely formulated within the discussion, it is often buried in a large amount of text, making it difficult to comprehend, which delays its implementation through the necessary repository changes. To guide developers in more easily absorbing information relevant towards making these changes and consequently expedite bug resolution, we investigate generating a concise natural language description of the solution by synthesizing relevant content as it emerges in the discussion. We benchmark models for generating solution descriptions and design a classifier for determining when sufficient context for generating an informative description becomes available. We investigate approaches for real-time generation, entailing separately trained and jointly trained classification and generation models. Furthermore, we also study techniques for deriving natural language context from bug report discussions and generated solution descriptions to guide models in generating suggested bug-resolving code changes.Computer Science

Dialog as a vehicle for lifelong learning of grounded language understanding systems

Natural language interfaces have the potential to make various forms of technology, including mobile phones and computers as well as robots or other machines such as ATMs and self-checkout counters, more accessible and less intimidating to users who are unfamiliar or uncomfortable with other types of interfaces. In particular, natural language understanding systems on physical robots face a number of challenges, including the need to ground language in perception, the ability to adapt to changes in the environment and novel uses of language, and to deal with uncertainty in understanding. To effectively handle these challenges, such systems need to perform lifelong learning - continually updating the scope and predictions of the model with user interactions. In this thesis, we discuss ways in which dialog interaction with users can be used to improve grounded natural language understanding systems, motivated by service robot applications. We focus on two types of queries that can be used in such dialog systems – active learning queries to elicit knowledge about the environment that can be used to improve perceptual models, and clarification questions that confirm the system’s hypotheses, or elicit specific information required to complete a task. Our goal is to build a system that can learn how to interact with users balancing a quick completion of tasks desired by the user with asking additional active learning questions viiito improve the underlying grounded language understanding components. We present work on jointly improving semantic parsers from and learning a dialog policy for clarification dialogs, that improve a robot’s ability to understand natural language commands. We introduce the framework of opportunistic active learning, where a robot introduces opportunistic queries, that may not be immediately relevant, into an interaction in the hope of improving performance in future interactions. We demonstrate the usefulness of this framework in learning to ground natural language descriptions of objects, and learn a dialog policy for such interactions. We also learn dialog policies that balance task completion, opportunistic active learning, and attribute-based clarification questions. Finally, we attempt to expand this framework to different types of underlying models of grounded language understanding.Computer Science

Expanding Task Diversity in Explanation-Based Interactive Task Learning

The possibility of having artificial agents that can interact with humans and learn completely new tasks through instruction and demonstration is an exciting prospect. This is the goal of the emerging research area of Interactive Task Learning. Solving this problem requires integrating many capabilities across AI to create general robot learns that can operate in a variety of environments. One particular challenge is that the space of possible tasks is extremely large and varied. Developing approaches that cover this space is a difficult challenge, made more so by having to learn from a limited, though high-quality, number of examples given through interaction with a teacher. In this dissertation, we identify three major dimensions of task complexity (diverse types of actions, task formulations, and task modifiers), and describe extensions that demonstrate greater learning capabilities for each dimension than previous work. First, we extend the representations and learning mechanism for innate tasks so the agent can learn tasks that utilize many different types of actions beyond physical object manipulation, such as communication and mental operations. Second, we implement a novel goal-graph representation that supports both goal-based and procedural tasks. Thus the instructor can formulate a task as achieving a goal and let the agent use planning to execute it, or can formulate the task as executing a procedure, or sequence of steps, when it is not easy to define a goal. This also allows interesting cases of a task that blends elements of a procedure and goal. Third, we added support for learning subtasks with various modifying clauses, such as temporal constraints, conditions, or looping structures. Crucially, we show that the agent can learn and generalize a canonical version of a task and then combine it with these various modifiers within a task hierarchy without requiring additional instruction. This is done in the context of Rosie -- an agent implemented within the Soar cognitive architecture that can learn completely new tasks in one shot through situated interactive instruction. By leveraging explanation-based generalization and domain knowledge, the agent quickly learns new hierarchical tasks, including their structure, arguments, goals, execution policies, and task decompositions, through natural language instruction. It has been used with various robotic platforms, though most of the learning demonstrations and evaluations in this work use a simulated mobile robot in a multi-room, partially-observable environment. In the end, we show that the agent can combine all of these extensions while learning complex hierarchical tasks that cover extended periods of time and demonstrate significant flexibility.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168026/1/mininger_1.pd