Given the cost of HPC clusters, making best use of them is crucial to improve infrastructure ROI. Likewise, reducing failed HPC jobs and related waste in terms of user wait times is crucial to improve HPC user productivity (aka human ROI). While most efforts (e.g., debugging HPC programs) explore technical aspects to improve ROI of HPC clusters, we hypothesize non-technical (human) aspects are worth exploring to make non-trivial ROI gains; specifically, understanding non-technical aspects and how they contribute to the failure of HPC jobs. In this regard, we conducted a case study in the context of Beocat cluster at Kansas State University. The purpose of the study was to learn the reasons why users terminate jobs and to quantify wasted computations in such jobs in terms of system utilization and user wait time. The data from the case study helped identify interesting and actionable reasons why users terminate HPC jobs. It also helped confirm that user terminated jobs may be associated with non-trivial amount of wasted computation, which if reduced can help improve the ROI of HPC clusters.

Although Machine Learning (ML) based approaches have shown promise for Android malware detection, a set of critical challenges remain unaddressed. Some of those challenges arise in relation to proper evaluation of the detection approach while others are related to the design decisions of the same. In this paper, we systematically study the impact of these challenges as a set of research questions (i.e., hypotheses). We design an experimentation framework where we can reliably vary several parameters while evaluating ML-based Android malware detection approaches. The results from the experiments are then used to answer the research questions. Meanwhile, we also demonstrate the impact of some challenges on some existing ML-based approaches. The large (market-scale) dataset (benign and malicious apps) we use in the above experiments represents the real-world Android app security analysis scale. We envision this study to encourage the practice of employing a better evaluation strategy and better designs of future ML-based approaches for Android malware detection.

When composing a system from components, we need to ensure that the components are compatible. This is commonly achieved by components interacting only via published and well-defined interfaces. Even so, it is possible for client components to learn about and depend on unpublished yet observable behaviors of components. To identify and support these situations, compatibility testing should uncover such observable behaviors.

In this paper, we focus on concurrent programs that use locks to achieve isolation of data accessed by critical sections of code. We present ISOLATOR, an algorithm that guarantees isolation for well-behaved threads of a program that obey a locking discipline even in the presence of ill-behaved threads that disobey the locking discipline. ISOLATOR uses code instrumentation, data replication, and virtual memory protection to detect isolation violations and delays ill-behaved threads to ensure isolation. Our instrumentation scheme requires access only to the code of well-behaved threads. We have evaluated ISOLATOR on several benchmark programs and found that ISOLATOR can ensure isolation with reasonable runtime overheads. In addition, we present three general desiderata - safety, isolation, and permissiveness - for any scheme that attempts to ensure isolation, and formally prove that ISOLATOR satisfies all of these desiderata.

The notion of control dependence underlies many program analysis and transformation techniques. Despite being widely used, existing definitions and approaches to calculating control dependence are difficult to apply directly to modern program structures because these make substantial use of exception processing and increasingly support reactive systems designed to run indefinitely. This article revisits foundational issues surrounding control dependence, and develops definitions and algorithms for computing several variations of control dependence that can be directly applied to modern program structures. To provide a foundation for slicing reactive systems, the article proposes a notion of slicing correctness based on weak bisimulation, and proves that some of these new definitions of control dependence generate slices that conform to this notion of correctness. This new framework of control dependence definitions, with corresponding correctness results, is even able to support programs with irreducible control flow graphs. Finally, a variety of properties show that the new definitions conservatively extend classic definitions. These new definitions and algorithms form the basis of the Indus Java slicer, a publicly available program slicer that has been implemented for full Java.

The Simple Object Access Protocol (SOAP) is a dominant enabling technology in the field of Web services. Web services demand high performance, security and extensibility. SOAP, being based on extensible markup language (XML), inherits not only the advantages of XML, but its relatively poor performance. This makes SOAP a poor choice for many high-performance Web services. We present a new approach to implementing the SOAP protocol using caching on the SOAP server. This approach has significant performance advantages over current approaches while maintaining complete protocol compliance. We demonstrate its practicality by implementing a demonstration system under Linux, giving speedups of over 250% in our sample applications.

The use of component models such as Enterprise Java Beans and the CORBA Component Model (CCM) in application development is expanding rapidly. Even in real-time safety/mission-critical domains, component-based development is beginning to take hold as a mechanism for incorporating non-functional aspects such as real-time, quality-of-service, and distribution. To form an effective basis for development of such systems, we believe that support for reasoning about correctness properties of component-based designs is essential. In this paper, we present Cadena - an integrated environment for building and modeling CCM systems. Cadena provides facilities for defining component types using CCM IDL, specifying dependency information and transition System semantics for these types, assembling systems from CCM components, visualizing various dependence relationships between components, specifying and verifying correctness properties of models of CCM systems derived from CCM IDL, component assembly information, and Cadena specifications, and producing CORBA stubs and skeletons implemented in Java. We are applying Cadena to avionics applications built using Boeing's Bold Stroke framework.

The use of component models such as Enterprise Java Beans and the CORBA Component Model (CCM) in application development is expanding rapidly. Even in real-time safety-critical and mission-critical domains, component-based development is beginning to take hold as a mechanism for in-corporating non-functional aspects such as real-time, quality-of-service, and distribution.