Concurrency bugs do significantly affect system reliability. Although many efforts have been made to address this problem, there are still many bugs that cannot be detected because of the complexity of concurrent programs. Compared with atomicity violations, order violations are always neglected. Efficient and effective approaches to detecting order violations are therefore in urgent need. This paper presents a bidirectional predictive trace analysis approach, BIPED, which can detect order violations in parallel based on a recorded program execution. BIPED collects an expected-order execution trace into a layered bidirectional prediction model, which intensively represents two types of expected-order data flows in the bottom layer and combines the lock sets and the bidirectionally order constraints in the upper layer. BIPED then recognizes two types of candidate violation intervals driven by the bottom-layer model and then checks these recognized intervals bidirectionally based on the upper-layer constraint model. Consequently, concrete schedules can be generated to expose order violation bugs. Our experimental results show that BIPED can effectively detect real order violation bugs and the analysis speed is 2.3x-10.9x and 1.24x-1.8x relative to the state-of-the-art predictive dynamic analysis approaches and hybrid model based static prediction analysis approaches in terms of order violation bugs.

When debugging bugs, programmers often prepare test cases to reproduce buggy behaviours. However, for concurrent programs, test cases alone are typically insufficient to reproduce buggy behaviours, due to the nondeterminism of multi-threaded executions. In literature, various approaches have been proposed to reproduce buggy behaviours for concurrency bugs deterministically, but to the best of our knowledge, they are still limited. In particular, we have recognized three debugging scenarios from programming practice, but existing approaches can handle only one of the scenarios. In this paper, we propose a novel approach, called SPDebugger, that provides finer-grained thread controlling over test cases, programs under test, and even third party library code, to reproduce the predesigned thread execution schedule. The evaluation shows that SPDebugger handles more debugging scenarios than the state-of-the-art tool, called IMUnit, with similar human effort.

A large body of research in the measurement of software complexity at code level has been conducted, but little effort has been made to measure the architectural-level complexity of a software system. In this paper, we propose some architectural-level metrics which are appropriate for evaluating the architectural attributes of a software system. The main feature of our approach is to assess the architectural-level complexity of a software system by analyzing its formal architectural specification, and therefore the process of metric computation can be automated completely.

The proliferation of diverse kinds of programming languages and platforms makes it a common need to have the same functionality implemented in different languages for different platforms, such as Java for Android applications and C# for Windows phone applications. Although versions of code written in different languages appear syntactically quite different from each other, they are intended to implement the same software and typically contain many code snippets that implement similar functionalities, which we call cross-language clones. When the version of code in one language evolves according to changing functionality requirements and/or bug fixes, its cross-language clones may also need be changed to maintain consistent implementations for the same functionality. Thus, it is needed to have automated ways to locate and track cross-language clones within the evolving software. In the literature, approaches for detecting cross-language clones are only for languages that share a common intermediate language (such as the .NET language family) because they are built on techniques for detecting single-language clones. To extend the capability of cross-language clone detection to more diverse kinds of languages, we propose a novel automated approach, CLCMiner, without the need of an intermediate language. It mines such clones from revision histories, based on our assumption that revisions to different versions of code implemented in different languages may naturally reflect how programmers change cross-language clones in practice, and that similarities among the revisions (referred to as clones in diffs or diff clones) may indicate actual similar code. We have implemented a prototype and applied it to ten open source projects implementations in both Java and C#. The reported clones that occur in revision histories are of high precisions (89% on average) and recalls (95% on average). Compared with token-based code clone detection tools that can treat code as plain texts, our tool can detect significantly more cross-language clones. All the evaluation results demonstrate the feasibility of revision-history based techniques for detecting cross-language clones without intermediates and point to promising future work.