Node.js has become one of the most popular development platforms due to its superior concurrency support. However, races induced by the nondeterministic execution order of event handlers may occur in Node.js applications, causing serious runtime failures. The state-of-the-art Node.js race detector NRace builds a happens-before (HB) graph before detection with a set of HB relation rules. In detection, NRace utilizes a heavy-weight BFS-based algorithm to query the reachability between resource operations, which introduces substantial overhead in practice, causing NRace inapplicable to real-world Node.js application test processes. This paper proposes a more practical Node.js dynamic race detection approach called NodeRT (Node.js Race Tracker). To reduce unnecessary overhead, NodeRT simplifies the HB relation rules, and divides the detection into three stages: trace collection stage, race candidate detection stage, and false positive removal stage. In the trace collection stage, NodeRT constructs a partial HB graph called asynchronous call tree (ACTree), enabling efficient reachability queries between event handlers. In the race candidate detection stage, NodeRT performs detection on the ACTree, which effectively eliminates most non-racing event handlers and outputs race candidates. In the false positive removal stage, NodeRT utilizes matching rules derived from HB relation rules and features of resources to reduce false positives in the race candidates. In experiments, NodeRT detects all known races and 9 unknown harmful races in real-world applications, whereas NRace only detects 3 of the unknown harmful races, with 64× more time consumption on average. Compared with NRace, NodeRT significantly reduces the overhead, making it practical to be integrated into real-world test processes.

Code comments provide abundant information that have been leveraged to help perform various software engineering tasks, such as bug detection, specification inference, and code synthesis. However, developers are less motivated to write and update comments, making it infeasible and error-prone to leverage comments to facilitate software engineering tasks. In this paper, we propose to leverage program analysis to systematically derive, refine, and propagate comments. For example, by propagation via program analysis, comments can be passed on to code entities that are not commented such that code bugs can be detected leveraging the propagated comments. Developers usually comment on different aspects of code elements like methods, and use comments to describe various contents, such as functionalities and properties. To more effectively utilize comments, a fine-grained and elaborated taxonomy of comments and a reliable classifier to automatically categorize a comment are needed. In this paper, we build a comprehensive taxonomy and propose using program analysis to propagate comments. We develop a prototype CPC, and evaluate it on 5 projects. The evaluation results demonstrate 41573 new comments can be derived by propagation from other code locations with 88% accuracy. Among them, we can derive precise functional comments for 87 native methods that have neither existing comments nor source code. Leveraging the propagated comments, we detect 37 new bugs in open source large projects, 30 of which have been confirmed and fixed by developers, and 304 defects in existing comments (by looking at inconsistencies between existing and propagated comments), including 12 incomplete comments and 292 wrong comments. This demonstrates the effectiveness of our approach. Our user study confirms propagated comments align well with existing comments in terms of quality.

Nowadays, Web services are widely used because of their interoperability and reusability. Multiple Web services can be composed following some business logic specified by BPEL (Business Process Execution Language) scripts. Since BPEL scripts allow specifying concurrent workflow, typical concurrency problems, such as data race, atomicity violation and order violation, also commonly occur in BPEL scripts. These issues are hard to detect and reproduce due to their non-determinism and the special language features of BPEL. In this paper, we implement a tool to detect data races for WS-BPEL based on static analysis approach and constraints solver. Our system is based on three key concepts: (1) a preprocess model to record necessary information, (2) a thorough Happens-Before model of WS-BPEL concurrency, (3) constraint encoding to transfer Happens-Before relationship to constraints and check if there is a feasible solution (namely data races) by Z3-Str solver. We evaluate the usability and performance of our tool on 10 benchmark programs with effective results.

The CC4 neural network is a new type of corner classification training algorithm for three-layered feed forward neural networks. CC4 is now successfully used in meta search engine Anvish. When the documents are almost of the same size, CC4 neural network is an effective document classification algorithm. However, there is great difference in document sizes in general, and CC4 use the whole dictionary as the space of vector which leads to a lot of documents represented by sparse vectors. This paper brings forward feature extraction based neural network GA-CC4. The method of GA feature extraction extracts the feature items really representing the documents in the document set, which are constructed as the set of feature items. Based on the set of feature items and combining the document frequency, the document can be represented. By this method, the dimensions representing the documents can be reduced, which can solve the precise problem caused by the different document sizes, and it can also map the scalar features to the Boolean input of the neural network by binary coding, by which the quality of input data of neural network is improved.

A meta Web search engine first sends a user's search requests to its referenced search engines. The results returned by these search engines are then integrated by one result-integrating algorithm and returned to the user. Query precision, completeness and response speed are directly affected by the choice of result integration algorithm. By analyzing common result-integrating algorithms, this paper recommends several unproved algorithms to improve the coherence of search results.

Web search engines are very useful information service tools in the Internet. The current Web search engines produce search results relating to the search terms and the actual information collected by them. Since the selections of the search results cannot affect the future ones, they may not cover most people's interests. In the paper, feedback information produced by the users' accessing lists is represented by a rough set and can influence the search results. Thus the search engines can provide self-adaptability.

To speed up fetching web pages, this paper gives an intelligent technology of web pre-fetching. We use a simplified WWW data model to represent the data in the cache of web browser to mine the association rules. We store these rules in a knowledge base so as to predict the user's actions. Intelligent agents are responsible for mining the users' interest and pre-fetching web pages, based on the interest association repository. In this way user browsing time has been reduced transparently.