Deadlocks are notorious bugs in multithreaded programs, causing serious reliability issues. However, they are difficult to be fully expunged before deployment, as their appearances typically depend on specific inputs and thread schedules, which require the assistance of dynamic tools. However, existing deadlock detection tools mainly focus on locks, but cannot detect deadlocks related to condition variables. This paper presents a novel approach to fill this gap. It extends the classic lock dependency to generalized dependency by abstracting the signal for the condition variable as a special resource so that communication deadlocks can be modeled as hold-and-wait cycles as well. It further designs multiple practical mechanisms to record and analyze generalized dependencies. In the end, this paper presents the implementation of the tool, called UnHang. Experimental results on real applications show that UnHang is able to find all known deadlocks and uncover two new deadlocks. Overall, UnHang only imposes around 3% performance overhead and 8% memory overhead, making it a practical tool for the deployment environment.

The abstract-machine model was developed by Chang to formalize the decision cycles of slow intelligence systems. It turns out that the selection of software process can also be regarded as a slow intelligence system. In this paper, we formalize abstract-machine prototypes for different software process models (SPMs) such as waterfall model, incremental model, spiral model, extreme programming model and scrum model. A Software Process Generator (SPG) was implemented to generate SPMs based upon design considerations. Initial evaluation by undergraduate students using SPG to learn software processes suggests further improvements to make it a useful learning tool.

Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and performance, the association of frequent items in large data mining requires both extensive time and effort, particularly when the datasets become increasingly larger. In the process of associated data mining in a big data environment, the MapReduce programming model is typically used to perform task partitioning and parallel processing, which could improve the execution efficiency of the algorithm. However, to ensure that the associated rule is not destroyed during task partitioning and parallel processing, the inner-relationship data must be stored in the computer space. Because inner-relationship data are redundant, storage of these data will significantly increase the space usage in comparison with the original dataset. In this study, we find that the formation of the frequent pattern (FP) mining algorithm depends mainly on the conditional pattern bases. Based on the parallel frequent pattern (PFP) algorithm theory, the grouping model divides frequent items into several groups according to their frequencies. We propose a non-group PFP (NG-PFP) mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks. Moreover, we present the NG-PFP algorithm for task partition and parallel processing, and its performance in the Hadoop cluster environment is analyzed and discussed. Experimental results indicate that the non-group model shows obvious improvement in terms of computational efficiency and the space utilization rate.

Machine Model was developed by Chang to formalize the decision cycles of slow intelligence systems.It turns out the selection of software process can also be regarded as a slow intelligence system.In this paper we formalize abstract-machine prototypes for different software process models such as waterfall model, incremental model, spiral model, extreme programming model and scrum model.A Software Process Generator SPG was implemented to generate software process models based upon design considerations.Initial evaluation by undergraduate students using SPG to learn software processes suggests further improvements to make it a useful learning tool.

Deadlocks are critical problems afflicting parallel applications, causing software to hang with no further progress. Existing detection tools suffer not only from significant recording performance overhead, but also from excessive memory and/or storage overhead. In addition, they may generate numerous false alarms. Subsequently, after problems have been reported, tremendous manual effort is required to confirm and fix these deadlocks. This paper designs a novel system, UnDead, that helps defeat deadlocks in production software. Different from existing detection tools, UnDead imposes negligible runtime performance overhead (less than 3 % on average) and small memory overhead (around 6%), without any storage consumption. After detection, UnDead automatically strengthens erroneous programs to prevent future occurrences of both existing and potential deadlocks, which is similar to the existing work-Dimmunix. However, UnDead exceeds Dimmunix with several orders of magnitude lower performance overhead, while eliminating numerous false positives. Extremely low runtime and memory overhead, convenience, and automatic prevention make UnDead an always-on detection tool, and a "band-aid" prevention system for production software.