Tien N. Nguyen

Graph-based mining of multiple object usage patterns

The interplay of multiple objects in object-oriented programming often follows specific protocols, for example certain orders of method calls and/or control structure constraints among them that are parts of the intended object usages. Unfortunately, the information is not always documented. That creates long learning curve, and importantly, leads to subtle problems due to the misuse of objects. In this paper, we propose GrouMiner, a novel graph-based approach for mining the usage patterns of one or multiple objects. GrouMiner approach includes a graph-based representation for multiple object usages, a pattern mining algorithm, and an anomaly detection technique that are efficient, accurate, and resilient to software changes. Our experiments on several real-world programs show that our prototype is able to find useful usage patterns with multiple objects and control structures, and to translate them into user-friendly code skeletons to assist developers in programming. It could also detect the usage anomalies that caused yet undiscovered defects and code smells in those programs.

A graph-based approach to API usage adaptation

Reusing existing library components is essential for reducing the cost of software development and maintenance. When library components evolve to accommodate new feature requests, to fix bugs, or to meet new standards, the clients of software libraries often need to make corresponding changes to correctly use the updated libraries. Existing API usage adaptation techniques support simple adaptation such as replacing the target of calls to a deprecated API, however, cannot handle complex adaptations such as creating a new object to be passed to a different API method, or adding an exception handling logic that surrounds the updated API method calls. This paper presents LIBSYNC that guides developers in adapting API usage code by learning complex API usage adaptation patterns from other clients that already migrated to a new library version (and also from the API usages within the librarys test code). LIBSYNC uses several graph-based techniques (1) to identify changes to API declarations by comparing two library versions, (2) to extract associated API usage skeletons before and after library migration, and (3) to compare the extracted API usage skeletons to recover API usage adaptation patterns. Using the learned adaptation patterns, LIBSYNC recommends the locations and edit operations for adapting API usages. The evaluation of LIBSYNC on real-world software systems shows that it is highly correct and useful with a precision of 100% and a recall of 91%.

Complete and accurate clone detection in graph-based models

Model-Driven Engineering (MDE) has become an important development framework for many large-scale software. Previous research has reported that as in traditional code-based development, cloning also occurs in MDE. However, there has been little work on clone detection in models with the limitations on detection precision and completeness. This paper presents ModelCD, a novel clone detection tool for Matlab/Simulink models, that is able to efficiently and accurately detect both exactly matched and approximate model clones. The core of ModelCD is two novel graph-based clone detection algorithms that are able to systematically and incrementally discover clones with a high degree of completeness, accuracy, and scalability. We have conducted an empirical evaluation with various experimental studies on many real-world systems to demonstrate the usefulness of our approach and to compare the performance of ModelCD with existing tools.

Refactoring-Aware Configuration Management for Object-Oriented Programs

Current text based software configuration management (SCM) systems have trouble with refactorings. Refactorings result in global changes and lead to merge conflicts. A refactoring-aware SCM system reduces merge conflicts, preserves program history better and makes it easier to understand program evolution. This paper describes MolhadoRef a refactoring-aware SCM system and the merge algorithm at its core. MolhadoRef records change operations (refactorings and edits) used to produce one version, and replays them when merging versions. Since refactorings are change operations with well defined semantics, MolhadoRef treats them intelligently. A case-study shows that MolhadoRef solves automatically more merge conflicts than CVS while resulting in fewer merge errors.

Duplicate bug report detection with a combination of information retrieval and topic modeling

Detecting duplicate bug reports helps reduce triaging efforts and save time for developers in fixing the same issues. Among several automated detection approaches, text-based information retrieval (IR) approaches have been shown to outperform others in term of both accuracy and time efficiency. However, those IR-based approaches do not detect well the duplicate reports on the same technical issues written in different descriptive terms. This paper introduces DBTM, a duplicate bug report detection approach that takes advantage of both IR-based features and topic-based features. DBTM models a bug report as a textual document describing certain technical issue(s), and models duplicate bug reports as the ones about the same technical issue(s). Trained with historical data including identified duplicate reports, it is able to learn the sets of different terms describing the same technical issues and to detect other not-yet-identified duplicate ones. Our empirical evaluation on real-world systems shows that DBTM improves the state-of-the-art approaches by up to 20% in accuracy.

Fuzzy set and cache-based approach for bug triaging

Bug triaging aims to assign a bug to the most appropriate fixer. That task is crucial in reducing time and efforts in a bug fixing process. In this paper, we propose Bugzie, a novel approach for automatic bug triaging based on fuzzy set and cache-based modeling of the bug-fixing expertise of developers. Bugzie considers a software system to have multiple technical aspects, each of which is associated with technical terms. For each technical term, it uses a fuzzy set to represent the developers who are capable/competent of fixing the bugs relevant to the corresponding aspect. The fixing correlation of a developer toward a technical term is represented by his/her membership score toward the corresponding fuzzy set. The score is calculated based on the bug reports that (s)he has fixed, and is updated as the newly fixed bug reports are available. For a new bug report, Bugzie combines the fuzzy sets corresponding to its terms and ranks the developers based on their membership scores toward that combined fuzzy set to find the most capable fixers. Our empirical results show that Bugzie achieves significantly higher accuracy and time efficiency than existing state-of-the-art approaches.

A topic-based approach for narrowing the search space of buggy files from a bug report

Locating buggy code is a time-consuming task in software development. Given a new bug report, developers must search through a large number of files in a project to locate buggy code. We propose BugScout, an automated approach to help developers reduce such efforts by narrowing the search space of buggy files when they are assigned to address a bug report. BugScout assumes that the textual contents of a bug report and that of its corresponding source code share some technical aspects of the system which can be used for locating buggy source files given a new bug report. We develop a specialized topic model that represents those technical aspects as topics in the textual contents of bug reports and source files, and correlates bug reports and corresponding buggy files via their shared topics. Our evaluation shows that BugScout can recommend buggy files correctly up to 45% of the cases with a recommended ranked list of 10 files.

Recurring bug fixes in object-oriented programs

Previous research confirms the existence of recurring bug fixes in software systems. Analyzing such fixes manually, we found that a large percentage of them occurs in code peers, the classes/methods having the similar roles in the systems, such as providing similar functions and/or participating in similar object interactions. Based on graph-based representation of object usages, we have developed several techniques to identify code peers, recognize recurring bug fixes, and recommend changes for code units from the bug fixes of their peers. The empirical evaluation on several open-source projects shows that our prototype, FixWizard, is able to identify recurring bug fixes and provide fixing recommendations with acceptable accuracy.

A statistical semantic language model for source code

Recent research has successfully applied the statistical n-gram language model to show that source code exhibits a good level of repetition. The n-gram model is shown to have good predictability in supporting code suggestion and completion. However, the state-of-the-art n-gram approach to capture source code regularities/patterns is based only on the lexical information in a local context of the code units. To improve predictability, we introduce SLAMC, a novel statistical semantic language model for source code. It incorporates semantic information into code tokens and models the regularities/patterns of such semantic annotations, called sememes, rather than their lexemes. It combines the local context in semantic n-grams with the global technical concerns/functionality into an n-gram topic model, together with pairwise associations of program elements. Based on SLAMC, we developed a new code suggestion method, which is empirically evaluated on several projects to have relatively 18-68% higher accuracy than the state-of-the-art approach.

Clone Management for Evolving Software

Recent research results suggest a need for code clone management. In this paper, we introduce JSync, a novel clone management tool. JSync provides two main functions to support developers in being aware of the clone relation among code fragments as software systems evolve and in making consistent changes as they create or modify cloned code. JSync represents source code and clones as (sub)trees in Abstract Syntax Trees, measures code similarity based on structural characteristic vectors, and describes code changes as tree editing scripts. The key techniques of JSync include the algorithms to compute tree editing scripts, to detect and update code clones and their groups, to analyze the changes of cloned code to validate their consistency, and to recommend relevant clone synchronization and merging. Our empirical study on several real-world systems shows that JSync is efficient and accurate in clone detection and updating, and provides the correct detection of the defects resulting from inconsistent changes to clones and the correct recommendations for change propagation across cloned code.