FEPSS: a flexible and extensible program comprehension support system

Maintenance of legacy systems and the advancement of reverse engineering techniques have placed a renewed interest in the program comprehension process. Descriptive studies of the program comprehension process have resulted in two seemingly opposing approaches. One approach is top-down in nature (consisting of hypothesis generation, decomposition, refinement, and verification) and the other is bottom-up in nature (consisting of low-level component comprehension, and the assembly and integration of related components). An empirical study is conducted to assess the exclusive nature of each approach during the comprehension process. The results refute the exclusive classification of program comprehension processes as either top-down or bottom-up. The constructs common between the approaches are assessed in an attempt to arrive at a reconciliation. A theory-based model of program comprehension is illuminated which is capable of accounting for the utilization of both top-down and bottom-up strategies within a single comprehension episode.

During the processing of an optical mirror, the performance parameters of the bottom support system would affect the surface forming accuracy of the mirror. The traditional bottom support system has a large unadjustable support stiffness, which increases the difficulty of unloading the impact force generated by the grinding disc. In response to this scenario, a flexible support system (FSS) consisting of 36 support cylinders with beryllium bronze reeds (BBRs) and rolling diaphragms (RDs) as key components is designed. It is necessary to analyze the key components of the support cylinder to reduce its axial movement resistance, ensure a consistent force output of each support point. First, the internal resistance model of a flexible support cylinder is established, and the main factors of internal resistance are then analyzed. Thereafter, the multi-objective structural parameters of the BBR and RD are simulated in ANSYS using the control variable method. The optimal structural parameters of BBR and RD are determined by simulation. Finally, experiments are performed on the RD ultimate pressure, internal resistance of the support cylinder, and consistency of the force output of the FSS. The experimental results show that the support cylinder with the optimized design has good force output consistency, which provides a theoretical basis for the application of FSS in optical mirror processing.

Despite recent advances and improvements in modeling and prediction of the dynamics of the machining process, an efficient machining process is limited due to chatter and instability of machining system. In fact, the machining system contains various kinds of joints, which cause difficulties in dynamics modeling, simulation and prediction. Moreover, the flexible support system results in large deformation and violent vibration of the workpiece when machining, and the thin-walled workpiece easily gives rise to the chatter of the machining system. Therefore, the dynamics of the flexible support system was considered to calculate stability lobe diagram in the modeling of milling process. The whole machining system was regarded as a closed loop composed by the machine tool structures, support, workpiece and machining process. In this paper, the receptance coupling (RC) method was introduced to predict the dynamics of the closed machining system. A milling process was taken for example to predict the chatter limitations using the dynamics of closed model. The mathematical model of the machining system (machine tool structures, spindle, holder and tool), together with the details of joint contacts, was given based on the RC method. The RC model was used to obtain the dynamics of the system, while receptance of the tool point was coupled. Based on the coupling model of the machining system, the depth limitations under different speeds were estimated for the technology parameter optimization in milling process. The response was considered to be the sum of the cutting point and the support system. The flexibility of the support system was considered to be the feedback of the cutting stiffness. By this means, the traditional model was modified to calculate the stability lobe diagram based on the dynamics of the spindle and support system. Furthermore, the milling experiment was carried out to verify the prediction results, and the dominant natural frequencies of receptance at tool point were obtained by modal testing to define the stability lobe diagram. It was found that the chatter results matched well with the stability lobes. It was concluded that the support system with poor stiffness might cause violent chatter especially when the workpiece was thin-walled. The cutting depth limitations of the flexible support system were lower than that of the rigid one. Moreover, this closed model of the machining system is appropriate for the chatter prediction of the flexible support system or thin-walled workpiece, so it is helpful for a better parameter optimization.

Many organizations and open-source projects use the C preprocessor (CPP) to implement configurability in their software systems. Despite extensive research, existing studies on the effects of CPP use on program comprehension are still limited to experiences, opinions, and empirical studies with narrow scopes. So, it is unclear whether the CPP actually leads to what is sometimes referred to as "#ifdef hell." In this paper, we expand the existing evidence on program comprehension in the presence of CPP directives, but we also highlight a surprising dilemma. We conducted an empirical study, including an experiment and a questionnaire, on the impact of refactoring CPP directives with 521 experienced software developers. The results indicate that, in contrast to previous findings, comprehension performance slightly worsened in terms of correctness when our participants worked on code with refactored CPP directives. However, in alignment with previous findings, they preferred the refactored code, considering it more comprehensible and easier to work with. This dilemma of objective performance versus subjective preference is a surprising outcome that has not been found before. We argue that our work motivates the need for more studies to understand this dilemma-which may significantly impact common beliefs in research and practice.

"Structured teaching" of TEACCH, a method of setting proper environment for people with autism, has also potentials for empowering people with intellectual disabilities. In order to empower each individual person, the structured teaching needs to be re-examined in the context of individualized and flexible support system. This paper defines the individualized and flexible supports of setting environment as "environmental support," and attempts to clarify the validity and the meaning of the structured teaching in the support activities for people with intellectual disabilities. Taking an intensive case study of a workshop in Penang, Malaysia, this paper focuses on 1) the individualization of client's activity space, 2) the improvement of the working processes, 3) the moving routes of the clients, 4) the changes of interrelationships between the clients and the supporters. The research results lead to the conclusion that an introduction of the structured teaching into the support activities at the workshop made it possible to construct the highly organized working environment, and to improve the staffs' attitude from "directive" to "facilitative," and enabled the clients to act spontaneously.

Seismic isolation aims to prevent the direct transmission of seismic wave energy to the main resistant structure. Typically, this is achieved by using a flexible support system that isolates the base of the structural system from the ground, absorbing the relative deformation at the soil–structure interface. Meanwhile, the main structure tends to move as a rigid body on this flexible support. This article proposes an alternative approach for the dynamic characterization of shredded rubber, which is used in geotechnical seismic isolation (GSI). Traditional testing methods are expensive and require specialized equipment, making them less practical for routine determination. The article details the most important parameters needed to evaluate the applicability and effectiveness of the material in the context of GSI. The parameter of interest, i.e. the transverse elasticity module GR , was calibrated numerically from an experimental model of a column of shredded rubber subjected to free vibrations tests. The results were consistent with those obtained from resonant column and hollow cylinder tests. In this way, it is shown that the presented approach is capable of providing valid estimations of the transverse modulus of elasticity of shredded rubber.

Program comprehension is an important task faced by developers during software maintenance. With the increasing complexity of evolving systems, program comprehension becomes more and more difficult. In practice, programmers are accustomed to getting a general view of the features in a software system and then finding the interesting or necessary files to start the understanding process. Given a system, developers may need a general view of the system. The traditional view of a system is shown in a package-class structure which is difficult to understand, especially for large systems. In this article, we focus on understanding the system in both feature view and file structure view. This article proposes an approach to generate a feature tree based on hierarchical Latent Dirichlet Allocation (hLDA), which includes two hierarchies, the feature hierarchy and file structure hierarchy. The feature hierarchy shows the features from abstract level to detailed level, while the file structure hierarchy shows the classes from whole to part. Empirical results show that the feature tree can produce a view for the features and files, and the clustering of classes in the package in our approach is better (in terms of recall) than the other clustering approach, that is, hierarchical clustering.

Aim of the new methodology proposed is to extract, from a behavioral circuit description, all information on the regularity of the computation. Array architectures optimally implement the extracted regular behavior. A high-level synthesis tool for such arrays has been defined. First, the problem of translating a hardware description language (either VHDL or Hardware C) into and internal representation useful for the exploration of different alternative architectures is afforded. Here the main problems relate to the different interpretations that can be given to an HDL description during the behavioral synthesis step. A set of design rules for writing the specifications has been identified. Then the internal design representation is studied. A hierarchical graph-based representation has been adopted which contains all information needed to identify possible regularities. The hierarchical structure is composed of a data flow graph (DFG) that holds information about data and operations performed and of a control flow graph (CFG) representing the condition clauses causing the choice of the computations to be performed. All information needed to identify the regular structures, can be derived from the DFG alone, while the information of the CFG is useful to correctly synthesize the control machine. The proposed high-level synthesis approach has been implemented into a module called Orpheus, which can be connected to the Olympus synthesis system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper an optimum algorithm to translate control flow graphs to dataflow graphs is proposed for dataflow execution of sequential programs. Some of the existing analysis methods restrict the specification of a program to be processed while others require a very high analysis cost. The algorithm proposed in this paper (CD translation algorithm), (1) with a very low cost, and (2) for any control structure that can be described by a control flow graph, (3) can generate dataflow programs that give an optimum dataflow execution. Furthermore, this proposed analysis algorithm is designed to handle task level control flow graphs as well as instruction level control flow graphs, which are accepted by the existing methods, so that optimum control is possible for task level dataflow execution.

An automated approach for noise identification to assist software architecture recovery techniques

Software engineers can use reverse engineering as a helpful tool during program comprehension. Various programs, for example, allow the generation of UML diagrams that depict the program's components visually. However, these diagrams are produced using static analysis alone and do not differentiate between the various degrees of coupling between components. In this work, we use program profiling to identify coupling intensity between the various program components. In addition, our approach is able to identify hidden coupling, not produced through reverse engineering and highlight the most important components in a large system. We have created an open source software analysis tool, named COMPOSITOR, that analyzes program traces produced through program profiling and we discuss here the application of our approach in a small and a larger Java program to draw conclusions on how these two different scenarios can be approached.

One of the major accompaniments of the globalization is the rapid growing of urban areas. At the end of the 1970th only 38% of world lived in cities, this number increased to more than 50% by 2008. In 2030 two third of all people worldwide are expected to live in cities, many of them in megacities. Urban sprawl is a major environmental concern affecting cities and urban. Urban sprawl depends on the socio-economic situation in the cities. Thus, reducing migration, sustainable handling of the limited resources and smart growth are acknowledged as key tasks for urban planning. Coping with these tasks requires precise and adaptive planning instruments. The presented study is part of the research project GAUS (Gaining Additional Urban Space) aiming at inventorying the open space available in urban environments and, moreover, providing flexible multi-criteria spatial decision support system for its development. The method is based on VHR (Very high resolution) optical satellite data (QuickBird (QB) and IKONOS (IK)) which is applied on three study areas: Berlin, Istanbul, and Ruhr Area. Object-based image analysis is applied to map land cover and land use and derive metrics describing urban form and inner-urban structure on multiple scales. The workflow has been standardized and leads to comparable results across different test sites and datasets. In intersection with available GIS (Geographical Information System) and local ancillary data, the outputs of image analysis serve as input for a multi-criteria spatial decision support system. Flexible multi-criteria spatial decision support (MC-SDSS) tool has been created by using MATLAB (Matrix Laboratory) software and its tools (Mapping toolbox etc.). Users can change their weights and parameters with this tool for their different study areas. Urban planners can use final suitability maps of this tool. Thus complex decisions are supported by numerical calculation and spatial visualization in order to come to objective solutions. This work contribute to close the gap between remote sensing methods and applied urban planning. © 2011 IEEE.

Automatic vectorization is crucial for improving the performance of computationally intensive programs. Existing compilers use conservative optimization strategies for automatic vectorization, which, in many cases, lead to the loss of vectorization opportunity. Studies have shown that the use of machine learning algorithms to build a performance prediction model is beneficial to improve the program performance. The model input is program features, and the output is the predicted optimization strategies or the program performance related to the optimization. In this paper, we focus on a computational intensive loop structure-tensor contraction, which is common in quantum chemical simulations. Most existing machine learning methods rely on control and data flow graphs as features to represent programs, but different tensor contraction kernels have the same control and data flow graphs. In addition, the existing methods often use the same kind of learning algorithm to construct a learning model, which is prone to overfitting and low-precision problems. In this paper, we propose an automatic vectorization performance enhancement method based on ensemble learning. We construct an ensemble learning model to predict the performance of tensor contraction kernels with different vectorization strategies and select the best vectorization strategy for the kernels. According to the storage access patterns of the tensor contraction kernels, we propose a static method for features representation. Based on the multi-algorithm ensemble learning strategy, we obtain better learning results than the single learning algorithm. The experimental results show that the prediction model achieves 88% and 87% prediction efficiency on two different platforms with different instruction sets, data types, and compilers. Compared with the existing methods, the prediction efficiency is greatly improved. In addition, the average peak performance is $2.96 \times $ of Intel ICC 12.0 and $2.98 \times $ of GCC 4.6 compiler, respectively.

Before performing software maintenance and evolution tasks, developers must expend significant effort to understand the design of the subject software system. Reverse engineering and reengineering tools can extract artifacts, such as class diagrams, from source code so that the effort expended on program comprehension can be greatly reduced. Choosing suitable and appropriate tools is itself a difficult process. Many metrics and benchmarks have been proposed to evaluate the quality of reengineering tools, but the process is still complicated and requires much human effort. Furthermore, ignored by most evaluation experiments and approaches is the understanding of differences and similarities of the output artifacts produced by different tools. This paper proposes a novel approach for tool evaluation making a direct comparison of the output artifacts of the chosen tools. We apply our methodology on class diagrams extracted from source code. We utilize a class matching model to automate the process. In this study, we use ten open-source programs as test cases and two open source reverse engineering tools Doxygen and StarUML. We evaluate the output of these two tools based on class matching given the same input. This approach, we believe, provides developers with good guidance in tool selection.

INTRODUCTION: The use of intrathecal morphine via a morphine drug delivery system has been shown as a promising alternative to the treatment of chronic pain. These programmable devices can deliver morphine via simple or flexible programs. Simple programs release morphine continuously at the same rate over a 24-hour period, whereas flexible programs are optimized to release different dosages at different times. METHODS: Patients were enrolled at St. James Mercy Hospital, University of Rochester, New York from February 2016 to February 2020. An out-patient intrathecal morphine trial was performed by injecting a single dose of 0.1-0.3 mg of preservative free morphine in the lumbar intrathecal space. Following at least 60% reduction in pain compared to baseline, a permanent intrathecal catheter and morphine pump were implanted. The drug delivery system was programmed to deliver morphine doses in a simple continuous or flexible mode. Follow up visits occurred between 12 and 60 months, and the efficacy of the flexible dosing was assessed. RESULTS: 58 patients enrolled (34 female, 24 male, median age 59), 46 patients were on flexible dose morphine and 12 were on simple dose morphine. Statistical analysis found a significant difference in pain reduction between the two groups (p &lt; 0.01). The flexible sample had a mean reduction in pain of 8.72 and the simple sample had a mean reduction in pain of 8.17 out of 10 points on the 0-10 pain scale. There were no infections or complications present in the study. CONCLUSION: While both flexible and simple program intrathecal morphine drug delivery system proved effective in reducing chronic pain, flexible programs were significantly more effective. A future multi-center study with a larger sample size is desired.