Learning a Static Analyzer from Data

To be practically useful, modern static analyzers must precisely model the effect of both, statements in the programming language as well as frameworks used by the program under analysis. While important, manually addressing these challenges is difficult for at least two reasons: (i) the effects on the overall analysis can be non-trivial, and (ii) as the size and complexity of modern libraries increase, so is the number of cases the analysis must handle. In this paper we present a new, automated approach for creating static analyzers: instead of manually providing the various inference rules of the analyzer, the key idea is to learn these rules from a dataset of programs. Our method consists of two ingredients: (i) a synthesis algorithm capable of learning a candidate analyzer from a given dataset, and (ii) a counter-example guided learning procedure which generates new programs beyond those in the initial dataset, critical for discovering corner cases and ensuring the learned analysis generalizes to unseen programs. We implemented and instantiated our approach to the task of learning JavaScript static analysis rules for a subset of points-to analysis and for allocation sites analysis. These are challenging yet important problems that have received significant research attention. We show that our approach is effective: our system automatically discovered practical and useful inference rules for many cases that are tricky to manually identify and are missed by state-of-the-art, manually tuned analyzers

Far-Term Exploration of Advanced Single-Aisle Subsonic Transport Aircraft Concepts

Far-term single-aisle class aircraft concepts for potential entry-into-service of 2045 were investigated using an Interactive Reconfigurable Matrix of Alternatives (IRMA) approach. The configurations identified through this design space exploration were then distilled into three advanced aircraft concepts best characterizing the prominent features identified through the IRMA exploration. These three aircraft concepts were then configured and sized for a 150-passenger capacity and a 3,500 nautical mile design mission. Mission block fuel burn was estimated and compared to a far-term conventional configuration baseline concept and a 2005 l. These comparisons suggest considerable potential improvements in fuel efficiency from the investigated advanced concepts

Exploitation of Dynamic Communication Patterns through Static Analysis

Abstract not provide

Analyzing and Visualizing Whole Program Architectures

This paper describes our work to develop new tool support for analyzing and visualizing the architecture of complete large-scale (millions or more lines of code) programs. Our approach consists of (i) creating a compact, accurate representation of a whole C or C++ program, (ii) analyzing the program in this representation, and (iii) visualizing the analysis results with respect to the program's architecture. We have implemented our approach by extending and combining a compiler infrastructure and a program visualization tool, and we believe our work will be of broad interest to those engaged in a variety of program understanding and transformation tasks. We have added new whole-program analysis support to ROSE [15, 14], a source-to-source C/C++ compiler infrastructure for creating customized analysis and transformation tools. Our whole-program work does not rely on procedure summaries; rather, we preserve all of the information present in the source while keeping our representation compact. In our representation, a million-line application fits in well less than 1 GB of memory. Because whole-program analyses can generate large amounts of data, we believe that abstracting and visualizing analysis results at the architecture level is critical to reducing the cognitive burden on the consumer of the analysis results. Therefore, we have extended Vizz3D [19], an interactive program visualization tool, with an appropriate metaphor and layout algorithm for representing a program's architecture. Our implementation provides developers with an intuitive, interactive way to view analysis results, such as those produced by ROSE, in the context of the program's architecture. The remainder of this paper summarizes our approach to whole-program analysis (Section 2) and provides an example of how we visualize the analysis results (Section 3)

Tool Support for Inspecting the Code Quality of HPC Applications

The nature of HPC application development encourages ad hoc design and implementation, rather than formal requirements analysis and design specification as is typical in software engineering. However, we cannot simply expect HPC developers to adopt formal software engineering processes wholesale, even while there is a need to improve software structure and quality to ensure future maintainability. Therefore, we propose tools that HPC developers can use at their discretion to obtain feedback on the structure and quality of their codes. This feedback would come in the form of code quality metrics and analyses, presented when necessary in intuitive and interactive visualizations. This paper summarizes our implementation of just such a tool, which we apply to a standard HPC benchmark as ''proof-of-concept.'

A rim-and-spoke hypothesis to explain the biomechanical roles for cytoplasmic intermediate filament networks

Textbook images of keratin intermediate filament (IF) networks in epithelial cells and the functional compromization of the epidermis by keratin mutations promulgate a mechanical role for this important cytoskeletal component. In stratified epithelia, keratin filaments form prominent radial spokes that are focused onto cell-cell contact sites, i.e. the desmosomes. In this Hypothesis, we draw attention to a subset of keratin filaments that are apposed to the plasma membrane. They form a rim of filaments interconnecting the desmosomes in a circumferential network. We hypothesize that they are part of a rim-and-spoke arrangement of IFs in epithelia. From our review of the literature, we extend this functional role for the subplasmalemmal rim of IFs to any cell, in which plasma membrane support is required, provided these filaments connect directly or indirectly to the plasma membrane. Furthermore, cytoplasmic IF networks physically link the outer nuclear and plasma membranes, but their participation in mechanotransduction processes remain largely unconsidered. Therefore, we also discuss the potential biomechanical and mechanosensory role(s) of the cytoplasmic IF network in terms of such a rim (i.e. subplasmalemmal)-and-spoke arrangement for cytoplasmic IF networks

Inducing safer oblique trees without costs

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significantly higher than in the other class. Several authors have tackled this problem by developing cost-sensitive decision tree learning algorithms or have suggested ways of changing the distribution of training examples to bias the decision tree learning process so as to take account of costs. A prerequisite for applying such algorithms is the availability of costs of misclassification. Although this may be possible for some applications, obtaining reasonable estimates of costs of misclassification is not easy in the area of safety. This paper presents a new algorithm for applications where the cost of misclassifications cannot be quantified, although the cost of misclassification in one class is known to be significantly higher than in another class. The algorithm utilizes linear discriminant analysis to identify oblique relationships between continuous attributes and then carries out an appropriate modification to ensure that the resulting tree errs on the side of safety. The algorithm is evaluated with respect to one of the best known cost-sensitive algorithms (ICET), a well-known oblique decision tree algorithm (OC1) and an algorithm that utilizes robust linear programming

Finding a short and accurate decision rule in disjunctive normal form by exhaustive search

Greedy approaches suffer from a restricted search space which could lead to suboptimal classifiers in terms of performance and classifier size. This study discusses exhaustive search as an alternative to greedy search for learning short and accurate decision rules. The Exhaustive Procedure for LOgic-Rule Extraction (EXPLORE) algorithm is presented, to induce decision rules in disjunctive normal form (DNF) in a systematic and efficient manner. We propose a method based on subsumption to reduce the number of values considered for instantiation in the literals, by taking into account the relational operator without loss of performance. Furthermore, we describe a branch-and-bound approach that makes optimal use of user-defined performance constraints. To improve the generalizability we use a validation set to determine the optimal length of the DNF rule. The performance and size of the DNF rules induced by EXPLORE are compared to those of eight well-known rule learners. Our results show that an exhaustive approach to rule learning in DNF results in significantly smaller classifiers than those of the other rule learners, while securing comparable or even better performance. Clearly, exhaustive search is computer-intensive and may not always be feasible. Nevertheless, based on this study, we believe that exhaustive search should be considered an alternative for greedy search in many problems