Visible to the public Biblio

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Tingting Yu, Witawas Srisa-an, Gregg Rothermel.  2017.  An automated framework to support testing for process-level race conditions. Software: Testing, Verification, and Reliability .

Race conditions are difficult to detect because they usually occur only under specific execution interleavings. Numerous program analysis and testing techniques have been proposed to detect race conditions between threads on single applications. However, most of these techniques neglect races that occur at the process level due to complex system event interactions. This article presents a framework, SIMEXPLORER, that allows engineers to effectively test for process-level race conditions. SIMEXPLORER first uses dynamic analysis techniques to observe system execution, identify program locations of interest, and report faults related to oracles. Next, it uses virtualization to achieve the fine-grained controllability needed to exercise event interleavings that are likely to expose races. We evaluated the effectiveness of SIMEXPLORER on 24 real-world applications containing both known and unknown process-level race conditions. Our results show that SIMEXPLORER is effective at detecting these race conditions, while incurring an overhead that is acceptable given its effectiveness improvements.

Junjie Qian, Hong Jiang, Witawas Srisa-an, Sharad Seth.  2017.  Energy-efficient I/O Thread Schedulers for NVMe SSDs on NUMA. CCGrid '17 Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the stateof-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linuxbased NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic “lesser evil” algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energyefficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.

Yutaka Tsutano, Shakthi Bachala, Witawas Srisa-an, Gregg Rothermel, Jackson Dinh.  2017.  An Efficient, Robust, and Scalable Approach for Analyzing Interacting Android Apps. 39th International Conference on Software Engineering.

When multiple apps on an Android platform interact, faults and security vulnerabilities can occur. Software engineers need to be able to analyze interacting apps to detect such problems. Current approaches for performing such analyses, however, do not scale to the numbers of apps that may need to be considered, and thus, are impractical for application to realworld scenarios. In this paper, we introduce JITANA, a program analysis framework designed to analyze multiple Android apps simultaneously. By using a classloader-based approach instead of a compiler-based approach such as SOOT, JITANA is able to simultaneously analyze large numbers of interacting apps, perform on-demand analysis of large libraries, and effectively analyze dynamically generated code. Empirical studies of JITANA show that it is substantially more efficient than a state-of-theart approach, and that it can effectively and efficiently analyze complex apps including Facebook, Pokemon Go, and Pandora ´ that the state-of-the-art approach cannot handle.

Lichao Sun, Zhiqiang Li, Qiben Yan, Witawas Srisa-an, Yu Pan.  2016.  SigPID: Significant Permission Identification for Android Malware Detection. 11th International Conference on Malicious and Unwanted Software (MALCON 2016).

A recent report indicates that a newly developed mali- cious app for Android is introduced every 11 seconds.  To combat this alarming rate of malware creation,  we need a scalable malware detection approach that is effective and efficient. In this paper, we introduce SIGPID, a malware detection system based on permission  analysis to cope with the rapid increase in the number of Android malware. In- stead of analyzing all 135 Android permissions, our ap- proach applies 3-level pruning by mining the permission data to identify only significant permissions that can be ef- fective in distinguishing benign and malicious apps. SIG- PID then utilizes classification algorithms to classify differ- ent families of malware and benign apps. Our evaluation finds that only 22 out of 135 permissions are significant. We then compare the performance of our approach, using only

22 permissions, against a baseline approach that analyzes all permissions. The results indicate that when Support Vec- tor Machine (SVM) is used as the classifier, we can achieve over 90% of precision, recall, accuracy, and F-measure, which  are about the same as those produced by the base- line approach while incurring the analysis times that are 4 to 32 times smaller that those of using all 135 permissions. When we compare the detection effectiveness of SIGPID to those of other approaches, SIGPID can detect 93.62% of malware in the data set, and 91.4% unknown malware.

Zhiqiang Li, Lichao Sun, Qiben Yan, Witawas Srisa-an, Zhenxiang Chen.  2016.  DroidClassifier: Efficient Adaptive Mining of Application-Layer Header for Classifying Android Malware. 12th EAI International Conference on Security and Privacy in Communication Networks.

A recent report has shown that there are more than 5,000 malicious applications created for Android devices each day. This creates a need for researchers to develop effective and efficient malware classification and detection approaches. To address this need, we introduce DroidClassifier: a systematic framework for classifying network traffic generated by mobile malware. Our approach utilizes network traffic analysis to construct multiple models in an automated fashion using a supervised method over a set of labeled malware network traffic (the training dataset). Each model is built by extracting common identifiers from multiple HTTP header fields. Adaptive thresholds are designed to capture the disparate characteristics of different malware families. Clustering is then used to improve the classification efficiency. Finally, we aggregate the multiple models to construct a holistic model to conduct cluster-level malware classification. We then perform a comprehensive evaluation of DroidClassifier by using 706 malware samples as the training set and 657 malware samples and 5,215 benign apps as the testing set. Collectively , these malicious and benign apps generate 17,949 network flows. The results show that DroidClassifier successfully identifies over 90% of different families of malware with more than 90% accuracy with accessible computational cost. Thus, DroidClassifier can facilitate network management in a large network, and enable unobtrusive detection of mobile malware. By focusing on analyzing network behaviors, we expect DroidClassifier to work with reasonable accuracy for other mobile platforms such as iOS and Windows Mobile as well.

Supat Rattanasuksun, Tingting Yu, Witawas Srisa-an, Gregg Rothermel.  2016.  RRF: A Race Reproduction Framework for Use in Debugging Process-Level Races. 27th International Symposium on Software Reliability Engineering (ISSRE).

Process-level races are endemic in modern  systems. These races are difficult  to debug  because they are  sensitive to execution   events  such  as  interrupts and scheduling.  Unless  a process interleaving   that can result in the race can be found, it cannot be reproduced  and cannot be corrected. In practice, however,  the number of interleavings  that can occur among processes  in practice  is large,  and the patterns of interleavings can be complex. Thus, approaches for reproducing process-level races  to date are  often ineffective.  In  this paper, we present RRF, a race reproduction  framework that can help software engineers reproduce reported process-level races, enabling  them to potentially  debug these races. RRF performs a hybrid analysis by leveraging  existing  static program analysis tools, dynamic kernel event  reporting tools,  and yield points  to provide  the observability and controllability  needed to reproduce races. We conducted an empirical study to evaluate RRF; our results show that RRF can be effective for reproducing races.

Tingting Yu, Witawas Srisa-an, Gregg Rothermel.  2014.  SimRT: An Automated Framework to Support Regression Testing for Data Races. ICSE 2014 Proceedings of the 36th International Conference on Software Engineering.

Concurrent programs are prone to various classes of difficult-to-detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SimRT, an automated regression testing framework for use in detecting races introduced by code modifications. SimRT employs a regression test selection technique, focused on sets of program elements related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritization technique to improve the rate at which such races are detected. Our empirical study of SimRT reveals that it is more efficient and effective for revealing races than other approaches, and that its constituent test selection and prioritization components each contribute to its performance.

Junjie Qian, Witawas Srisa-an, Hong Jiang, Sharad Seth, Du Li, Pan Yi.  2016.  Exploiting FIFO Scheduler to Improve Parallel Garbage Collection Performance.. VEE '16 12th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments.

Recent studies have found that parallel garbage collection performs worse with more CPUs and more collector threads. As part of this work, we further investigate this enomenon and find that poor scalability is worst in highly scalable Java applications. Our investigation to find the causes clearly reveals that efficient multi-threading in an application can prolong the average object lifespan, which results in less effective garbage collection. We also find that prolonging lifespan is the direct result of Linux's Completely Fair Scheduler due to its round-robin like behavior that can increase the heap contention between the application threads. Instead, if we use pseudo first-in-first-out to schedule application threads in large multicore systems, the garbage collection scalability is significantly improved while the time spent in garbage collection is reduced by as much as 21%. The average execution time of the 24 Java applications used in our study is also reduced by 11%. Based on this observation, we propose two approaches to optimally select scheduling policies based on application scalability profile. Our first approach uses the profile information from one execution to tune the subsequent executions. Our second approach dynamically collects profile information and performs policy selection during execution.

Junjie Qian, Witawas Srisa-an, Du Li, Hong Jiang, Sharad Seth, Yaodong Yang.  2015.  SmartStealing: Analysis and Optimization of Work Stealing in Parallel Garbage Collection for Java VM.. Principles and Practice of Programming in Java (PPPJ).

Parallel garbage collection has been used to speedup the collection process on multicore architectures. Similar to other parallel techniques, balancing the workload among threads is critical to ensuring good overall collection performance. To this end, work stealing is employed by the current stateof-the-art Java Virtual Machine, OpenJDK, to keep GC threads from idling during a collection process. However, we found that the current algorithm is not efficient. Its usage can often cause GC performance to be worse than when work stealing is not used. In this paper, we identify three factors that affect work stealing efficiency: determining tasks that can benefit from stealing, frequency with which to attempt stealing, and performance impacts of failed stealing attempts. Based on this analysis, we propose SmartStealing, a new algorithm that can automatically decide whether to attempt stealing at a particular point during execution. If stealing is attempted, it can efficiently identify a task to steal from. We then compare the collection performances when (i) the default work stealing algorithm is used, (ii) work stealing is not used at all, and (iii) the SmartStealing approach is used. Without modifying the remaining garbage collection system, the evaluation result shows that SmartStealing can reduce the parallel GC execution time for 19 of the 21 benchmarks. The average reduction is 50.4% and the highest reduction is 78.7%. We also investigate the performances of SmartStealing on NUMA and UMA architectures.