42 research outputs found

    TRACEM - Towards a Standard Metamodel for Execution Traces in Model-Driven Reverse Engineering

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    Reverse engineering is a crucial stage in the software modernization process. The current techniques available in existing CASE tools provide forward engineering and limited facilities for reverse engineering, dynamic analysis in particular. The Architecture-Driven Modernization initiative has defined standards to support the modernization process in the model-driven engineering (MDE) context. Standardization increases interoperability between different tools enabling a new generation of solutions to benefit the whole industry and encourage collaboration among complementary vendors. In this paper, we present TRACEM, a metamodel to represent trace information under a standard representation. This metamodel complements a MDE framework for software modernization that aims to integrate static and dynamic analysis techniques during the reverse engineering process. This paper includes a case study that exemplifies how dynamic information combined with static information allows improving the whole reverse engineering process.XIX Workshop Ingeniería de Software (WIS)Red de Universidades con Carreras en Informátic

    Dynamic decision tree for legacy use-case recovery

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    In the context of reverse-engineering project we designed a use-case specification recovery technique for legacy information systems. With our technique, we can recover the alternative flows of each use-case of the system. It is based on a dynamic (i.e. runtime) analysis of the working of the system using execution traces. But "traditional" execution trace format do not contain enough information for this approach to work. Then we designed a new execution trace format together with the associated tool to get the program's dynamic decision tree corresponding to each of the use-case scenario. These trees are then processed to find the possible variants from the main scenario of each use-case. In this paper we first present our approach to the use-case specification recovery technique and the new trace format we designed. Then the decision tree compression technique is showed with a feasibility study. The contribution of the paper is our approach to the recovery of legacy systems' use-case, the new trace format and the decision tree processing technique

    Techniques To Facilitate the Understanding of Inter-process Communication Traces

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    High Performance Computing (HPC) systems play an important role in today’s heavily digitized world, which is in a constant demand for higher speed of calculation and performance. HPC applications are used in multiple domains such as telecommunication, health, scientific research, and more. With the emergence of multi-core and cloud computing platforms, the HPC paradigm is quickly becoming the design of choice of many service providers. HPC systems are also known to be complex to debug and analyze due to the large number of processes they involve and the way these processes communicate with each other to perform specific tasks. As a result, software engineers must spend extensive amount of time understanding the complex interactions among a system’s processes. This is usually done through the analysis of execution traces generated from running the system at hand. Traces, however, are very difficult to work with due to the overwhelming size of typical traces. The objective of this research is to present a set of techniques that facilitates the understanding of the behaviour of HPC applications through the analysis of system traces. The first technique consists of building an exchange format called MTF (MPI Trace Format) for representing and exchanging traces generated from HPC applications based on the MPI (Message Passing Interface) standard, which is a de facto standard for inter-process communication for high performance computing systems. The design of MTF is validated against well-known requirements for a standard exchange format. The second technique aims to facilitate the understanding of large traces of inter-process communication by automatically extracting communication patterns that characterize their main behaviour. Two algorithms are presented. The first one permits the recognition of repeating patterns in traces of MPI (Message Passing Interaction) applications whereas the second algorithm searches if a given communication pattern occurs in a trace. Both algorithms are based on the n-gram extraction technique used in natural language processing. Finally, we developed a technique to abstract MPI traces by detecting the different execution phases in a program based on concepts from information theory. Using this approach, software engineers can examine the trace as a sequence of high-level computational phases instead of a mere flow of low-level events. The techniques presented in this thesis have been tested on traces generated from real HPC programs. The results from several case studies demonstrate the usefulness and effectiveness of our techniques

    Helena

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    Ensemble-based systems are software-intensive systems consisting of large numbers of components which can dynamically form goal-oriented communication groups. The goal of an ensemble is usually achieved through interaction of some components, but the contributing components may simultaneously participate in several collaborations. With standard component-based techniques, such systems can only be described by a complex model specifying all ensembles and participants at the same time. Thus, ensemble-based systems lack a development methodology which particularly addresses the dynamic formation and concurrency of ensembles as well as transparency of participants. This thesis proposes the Helena development methodology. It slices an ensemble-based system in two dimensions: Each kind of ensemble is considered separately. This allows the developer to focus on the relevant parts of the system only and abstract away those parts which are non-essential to the current ensemble. Furthermore, an ensemble itself is not defined solely in terms of participating components, but in terms of roles which components adopt in that ensemble. A role is the logical entity needed to contribute to the ensemble while a component provides the technical functionalities to actually execute a role. By simultaneously adopting several roles, a component can concurrently participate in several ensembles. Helena addresses the particular challenges of ensemble-based systems in the main development phases: The domain of an ensemble-based system is described as an ensemble structure of roles built on top of a component-based platform. Based on the ensemble structure, the goals of ensembles are specified as linear temporal logic formulae. With these goals in mind, the dynamic behavior of the system is designed as a set of role behaviors. To show that the ensemble participants actually achieve the global goals of the ensemble by collaboratively executing the specified behaviors, the Helena model is verified against its goals with the model-checker Spin. For that, we provide a translation of Helena models to Promela, the input language of Spin, which is proven semantically correct for a kernel part of Helena. Finally, we provide the Java framework jHelena which realizes all Helena concepts in Java. By implementing a Helena model with this framework, Helena models can be executed according to the formal Helena semantics. To support all activities of the Helena development methodology, we provide the Helena workbench as a tool for specification and automated verification and code generation. The general applicability of Helena is backed by a case study of a larger software system, the Science Cloud Platform. Helena is able to capture, verify and implement the main characteristics of the system. Looking at Helena from a different angle shows that the Helena idea of roles is also well-suited to realize adaptive systems changing their behavioral modes based on perceptions. We extend the Helena development methodology to adaptive systems and illustrate its applicability at an adaptive robotic search-and-rescue example

    Reducing Instrumentation Overhead when Reverse-Engineering Object Interactions

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    Reverse-engineering object interactions from source code can be done through static, dynamic, or hybrid (static plus dynamic) analyses. In the latter two, monitoring a program and collecting runtime information translates into some overhead during program execution. Depending on the type of application, the imposed overhead can reduce the precision and accuracy of the reverse-engineered object interactions (the larger the overhead the less precise or accurate the reverse-engineered interactions), to such an extent that the reverse-engineered interactions may not be correct, especially when reverse-engineering a multithreaded software system. One is therefore seeking an instrumentation strategy as less intrusive as possible. In our past work, we showed that a hybrid approach is one step towards such a solution, compared to a purely dynamic approach, and that there is room for improvements. In this paper, we uncover, in a systematic way, other aspects of the dynamic analysis that can be improved to further reduce runtime overhead, and study alternative solutions. Our experiments show effective overhead reduction thanks to a modified procedure to collect runtime information

    Machine Learning

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    Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

    Helena

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    Ensemble-based systems are software-intensive systems consisting of large numbers of components which can dynamically form goal-oriented communication groups. The goal of an ensemble is usually achieved through interaction of some components, but the contributing components may simultaneously participate in several collaborations. With standard component-based techniques, such systems can only be described by a complex model specifying all ensembles and participants at the same time. Thus, ensemble-based systems lack a development methodology which particularly addresses the dynamic formation and concurrency of ensembles as well as transparency of participants. This thesis proposes the Helena development methodology. It slices an ensemble-based system in two dimensions: Each kind of ensemble is considered separately. This allows the developer to focus on the relevant parts of the system only and abstract away those parts which are non-essential to the current ensemble. Furthermore, an ensemble itself is not defined solely in terms of participating components, but in terms of roles which components adopt in that ensemble. A role is the logical entity needed to contribute to the ensemble while a component provides the technical functionalities to actually execute a role. By simultaneously adopting several roles, a component can concurrently participate in several ensembles. Helena addresses the particular challenges of ensemble-based systems in the main development phases: The domain of an ensemble-based system is described as an ensemble structure of roles built on top of a component-based platform. Based on the ensemble structure, the goals of ensembles are specified as linear temporal logic formulae. With these goals in mind, the dynamic behavior of the system is designed as a set of role behaviors. To show that the ensemble participants actually achieve the global goals of the ensemble by collaboratively executing the specified behaviors, the Helena model is verified against its goals with the model-checker Spin. For that, we provide a translation of Helena models to Promela, the input language of Spin, which is proven semantically correct for a kernel part of Helena. Finally, we provide the Java framework jHelena which realizes all Helena concepts in Java. By implementing a Helena model with this framework, Helena models can be executed according to the formal Helena semantics. To support all activities of the Helena development methodology, we provide the Helena workbench as a tool for specification and automated verification and code generation. The general applicability of Helena is backed by a case study of a larger software system, the Science Cloud Platform. Helena is able to capture, verify and implement the main characteristics of the system. Looking at Helena from a different angle shows that the Helena idea of roles is also well-suited to realize adaptive systems changing their behavioral modes based on perceptions. We extend the Helena development methodology to adaptive systems and illustrate its applicability at an adaptive robotic search-and-rescue example
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