Giriprasad Sridhara

Automatic generation of natural language summaries for Java classes

Most software engineering tasks require developers to understand parts of the source code. When faced with unfamiliar code, developers often rely on (internal or external) documentation to gain an overall understanding of the code and determine whether it is relevant for the current task. Unfortunately, the documentation is often absent or outdated. This paper presents a technique to automatically generate human readable summaries for Java classes, assuming no documentation exists. The summaries allow developers to understand the main goal and structure of the class. The focus of the summaries is on the content and responsibilities of the classes, rather than their relationships with other classes. The summarization tool determines the class and method stereotypes and uses them, in conjunction with heuristics, to select the information to be included in the summaries. Then it generates the summaries using existing lexicalization tools. A group of programmers judged a set of generated summaries for Java classes and determined that they are readable and understandable, they do not include extraneous information, and, in most cases, they are not missing essential information.

Automatically Detecting the Up-To-Date Status of ToDo Comments in Java Programs

Easing program comprehension facilitates software maintenance, which consumes a disproportionate amount of resources within software development. Studies have shown that good comments can help in program comprehension. Among the different varieties of comments, TODO comments are used by developers to denote pending tasks. A developer may perform the task mentioned in the TODO comment but may forget to remove it, leading to obsolete comments. Such obsolete comments can hinder comprehension. Detecting such obsolete comments manually is tedious and error-prone. Thus, we need a tool to automatically check the status of TODO comments. We present a novel technique to automatically detect the status of a TODO comment. Given a method with a TODO comment, our TODO comment status checker uses information retrieval, linguistics and semantics to check if the comment is up to date. According to experienced programmers who judged our status checker, we achieve good accuracy, precision and recall.

ReAct: A System for Recommending Actions for Rapid Resolution of IT Service Incidents

In this paper, we present a system called ReAct which, given a problem/incident description, helps the service agents to easily identify set of actions and the possible action sequence to resolve the issue mentioned in the ticket. Th eframework uses unstructured text analysis on historical ticket data to find the next best action steps and uses visualization to help user choose the most suitable option.

Automatic problem extraction and analysis from unstructured text in IT tickets

IT services are extremely human labor intensive, and a key focus is to provide efficient services at low cost. Automation of repeatable IT tasks using software service agents that reduce human effort is therefore an important component of service management. A large fraction of the work done by IT service personnel involves troubleshooting of problems. However, the complexity of IT systems makes automated problem determination and resolution a challenging research problem. Using a database of prior customer problems and solutions, we build a system that extracts knowledge about different classes of problems arising in the IT infrastructure, mine problem linkages to recent system changes, and identify the resolution activities to mitigate problems. The system, at its core, uses data mining, machine learning, and natural language parsing techniques. By using extracted knowledge, one can (i) understand the kind of problems and the root causes affecting the IT infrastructure, (ii) proactively remediate the causes so that they no longer result in problems, and (iii) estimate the scope for automation for service management. In the future, a large cost differentiator for any IT company will often involve being able to build automated service agents from these technologies, which will result in a reduction in human effort.

Automated modularization of GUI test cases

Test cases that drive an application under test via its graphical user interface (GUI) consist of sequences of steps that perform actions on, or verify the state of, the application user interface. Such tests can be hard to maintain, especially if they are not properly modularized - that is, common steps occur in many test cases, which can make test maintenance cumbersome and expensive. Performing modularization manually can take up considerable human effort. To address this, we present an automated approach for modularizing GUI test cases. Our approach consists of multiple phases. In the first phase, it analyzes individual test cases to partition test steps into candidate subroutines, based on how user-interface elements are accessed in the steps. This phase can analyze the test cases only or also leverage execution traces of the tests, which involves a cost-accuracy tradeoff. In the second phase, the technique compares candidate subroutines across test cases, and refines them to compute the final set of subroutines. In the last phase, it creates callable subroutines, with parameterized data and control flow, and refactors the original tests to call the subroutines with context-specific data and control parameters. Our empirical results, collected using open-source applications, illustrate the effectiveness of the approach.

Automated Quality Assessment of Unstructured Resolution Text in IT Service Systems

In customer-care service centers, upon remediation of customer issues, the human agents are expected to record their resolution summary in a clear, concise and understandable manner. These resolution summaries create a rich untapped source of unstructured information. In this work, we have addressed the problem of how to enable human agents to write better quality resolution text. This helps curate data artifacts which can reduce problem diagnosis time and create repeatable resolution recipes by a cognitive system. The problem is addressed through a two pronged approach: (i) On the fly automated scoring of the agent’s resolution summary and (ii) identifying concrete areas of improvement in the summary and offering appropriate recommendations. The model for automatic scoring is derived from a feature set that encodes all significant and relevant aspects of the domain and text. The model is trained using annotated data and achieves an accuracy of 88.2 % which is a significant improvement over naive method of text based classification (68.5 %).