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Metaheuristic search techniques have been extensively used to automate the process of generating test cases and thus providing solutions for a more cost-effective testing process. This approach to test automation, often coined as “Search-based Software Testing” (SBST), has been used for a wide variety of test case generation purposes. Since SBST techniques are heuristic by nature, they must be empirically investigated in terms of how costly and effective they are at reaching their test objectives and whether they scale up to realistic development artifacts. However, approaches to empirically study SBST techniques have shown wide variation in the literature. This paper presents the results of a systematic, comprehensive review that aims at characterizing how empirical studies have been designed to investigate SBST cost-effectiveness and what empirical evidence is available in the literature regarding SBST cost-effectiveness and scalability. We also provide a framework that drives the data collection process of this systematic review and can be the starting point of guidelines on how SBST techniques can be empirically assessed. The intent is to aid future researchers doing empirical studies in SBST by providing an unbiased view of the body of empirical evidence and by guiding them in performing well designed empirical studies.

Program input syntactic structure is essential for a wide range of applications such as test case generation, software debugging and network security. However, such important information is often not available (e.g., most malware programs make use of secret protocols to communicate) or not directly usable by machines (e.g., many programs specify their inputs in plain text or other random formats). Furthermore, many programs claim they accept inputs with a published format, but their implementations actually support a subset or a variant. Based on the observations that input structure is manifested by the way input symbols are used during execution and most programs take input with top-down or bottom-up grammars, we devise two dynamic analyses, one for each grammar category. Our evaluation on a set of real-world programs shows that our technique is able to precisely reverse engineer input syntactic structure from execution. We apply our technique to hierarchical delta debugging (HDD) and network protocol reverse engineering. Our technique enables the complete automation of HDD, in which programmers were originally required to provide input grammars, and improves the runtime performance of HDD. Our client study on network protocol reverse engineering also shows that our technique supersedes existing techniques.

IT system architectures and many other kinds of structured artifacts are often described by formal models or informal diagrams. In practice, there are often a number of versions of a model or diagram, such as a series of revisions, divergent variants, or multiple views of a system. Understanding how versions correspond or differ is crucial, and thus automated assistance for matching models and diagrams is essential. We have designed a framework for finding these correspondences automatically based on Bayesian methods. We represent models and diagrams as graphs whose nodes have attributes such as name, type, connections to other nodes, and containment relations, and we have developed probabilistic models for rating the quality of candidate correspondences based on various features of the nodes in the graphs. Given the probabilistic models, we can find high quality correspondences using search algorithms. Preliminary experiments focusing on architectural models suggest that the technique is promising.

The Verifying Compiler (VC) project is a core component of the Dependable Systems Evolution Grand Challenge. The VC offers the promise of automatically proving that a program or component is correct, where correctness is defined by program assertions. While several VC prototypes exist, all adopt a semantics for assertions that is unsound. This paper presents a consolidation of VC requirements analysis activities that, in particular, brought us to ask targeted VC customers what kind of semantics they wanted. Taking into account both practitioners’ needs and current technological factors, we offer recovery of soundness through an adjusted definition of assertion validity that matches user expectations and can be implemented practically using current prover technology. For decades there have been debates concerning the most appropriate semantics for program assertions. Our contribution here is unique in that we have applied fundamental software engineering techniques by asking primary stakeholders what they want and based on this, proposed a means of efficiently realizing the semantics stakeholders want using standard tools and techniques. We describe how support for the new semantics has been added to ESC/Java2, one of the most fully developed VC prototypes. Case studies demonstrate the effectiveness of the new semantics at uncovering previously indiscernible specification errors.

Hard real-time systems always choose not to use garbage collection in order to avoid their unpredictable executions. Much effort has been expended trying to build predictable garbage collectors which can provide both temporal and spatial guarantees. Unfortunately, most existing work leads to systems that cannot easily achieve a balance between temporal and spatial performances. Moreover, the scheduling of garbage collectors has not been integrated into modern real-time scheduling frameworks, which makes the benefits provided by the advancement of scheduling techniques very difficult to obtain. This paper argues that the existing design criteria for real-time garbage collectors do not reflect the unique requirements of flexible hard real-time systems. As a part of our design criteria, a new performance indicator is proposed to describe the capability of a real-time garbage collector to achieve a better balance between temporal and spatial performances. A hybrid garbage collection algorithm is designed accordingly which also uses dual priority scheduling algorithm to reclaim spare capacity whilst guaranteeing deadlines. The algorithm has been implemented and evaluated in a real-time Java environment.

Britain must celebrate its scientists, because if the voters do, then so will the politicians

National Science and Engineering Week – running now with 2,000-plus exhibitions, lectures, open days and debates for an expected audience of 1.5 million – began as a whistle in the dark. Back in 1994, the science minister, William Waldegrave, secured a derisory £100,000 for the first one, and it seemed like a gimmick.

The charge of cynicism was unfair: Waldegrave was that rare thing, a minister with a prior and genuine interest in science. But the gesture came near the end of a long period of devastation of an intellectual tradition that had delivered Newton, Faraday, Darwin, James Clerk Maxwell, Rutherford and one of the unsung giants of the 20th century, Paul Dirac. In 15 years of Conservative government, ambitious projects had been abandoned, long-established research teams broken up, laboratories closed, universities starved and institutions privatised. The asset-stripping continued for another three years and, by 1997, British science had a stagnant and impoverished culture, creaking equipment and demoralised personnel.

Paradoxically, it also had a lively national festival of science, engineering and technology, and a separate, slightly later funfair in Edinburgh, both of which attracted crowds of buzzing schoolchildren and delighted adults. The science community took Waldegrave’s crust not as a sop but a challenge, and began to campaign for the re-election of reason and curiosity to the national debate. Thatcherite logic had argued that, if the economy really needed research, the market would provide it. No such thing happened. France, Germany, Japan and the US went on increasing investment in R&D while Britain became the place for merchant bankers and estate agents. But a freshly politicised community had by then understood that, in a democracy, science had to speak up, and so – at their successive jamborees – scientists did just that. They spelled out how information technology was forging a society in which knowledge was the real capital, and economic growth the interest that it accrued.

Here we go again. Last week the Royal Society reminded us that, while British science again faces cuts, France, Germany and the US are spending more than ever. Meanwhile, the inventor James Dyson urged the Tories not to cut the tax credits that support R&D. Peter Mandelson showed some sign of listening in an interview at the weekend, but that anyone should even need to make the argument shows how quickly forgotten have been the lessons of the past 30 years. Instead of paying university bosses the super-salaries we report on today, Britain must celebrate its scientists, because if the voters do, then so, eventually, will the politicians. We need our science festivals more than ever.

Describing and managing activities, resources and constraints of software development processes is a challenging goal for many organizations. A first generation of Software Process Modeling Languages (SPMLs) has appeared in the nineties but failed to gain broad industrial support. Recently however, a second generation of SPMLs appeared, leveraging the strong industrial interest for modeling languages such as the UML. In this article, we propose a comparison of these UML-based SPMLs. While not exhaustive, this comparison concentrates on SPMLs most representative of the various alternative approaches, ranging from UML-based framework specializations to full-blown executable meta-modeling approaches. To support the comparison of these various approaches, we propose a frame gathering a set of requirements for process modeling, such as semantic richness, modularity, executability, conformity to the UML standard, and formality. Beyond discussing the relative merits of these approaches, we also evaluate the overall suitability of these UML based SPMLs for software process modeling. Finally, we discuss the impact of these approaches on the current state of the practice, and conclude with lessons we have learned in doing this comparison.

Recently, there has been a proliferation of service-based systems, i.e. software systems that are composed of autonomous services, but can also use software code. In order to support the development of these systems, it is necessary to have new methods, processes, and tools. In this paper we describe a UML-based framework to assist with the development of service-based systems. The framework adopts an iterative process in which software services that can provide functional and non-functional characteristics of a system being developed are discovered, and the identified services are used to re-formulate the design models of the system. The framework uses a query language to represent structural, behavioural, and quality characteristics of services to de identified, and a query processor to match the queries against service registries. The matching process is based on distance measurements between the queries and service specfications. A prototype tool has been implemented. The work has been evaluated in terms of recall, precision, and performance measurements.

Efficiently and effectively searching for similar videos is an important and non-trivial problem in content-based video search systems. In this paper, we propose a subspace symbolization approach, namely SUDS, for content-based search on very large video databases. The novelty of SUDS is that it explores the data distribution in subspaces to build a visual dictionary with which the video data are processed by deriving the string matching techniques with two-step data simplification. Specifically, we first propose an adaptive approach, called VLP, to divide the whole visual feature space into a series of subspaces of variable lengths, from which the dominant ones are selected. By clustering the video keyframes over each dominant subspace, a stable visual dictionary is built and a compact video representation model is eveloped by transforming each keyframe into a word that is a series of symbols in the dominant subspaces, and further each video into a sequence of words. Then, we present an innovative similarity measure called CVE, which adopts a complementary information compensation scheme based on the visual features and sequence ontext of videos. Finally, an efficient two-layered index strategy with a number of query optimizations is proposed to facilitate video search. The experimental results demonstrate the high effectiveness and efficiency of SUDS.

This paper presents a knowledge discovery framework for the construction of Community Web Directories, a concept that we introduced in our recent work, applying personalization to Web directories. In this context, the Web directory is viewed as a thematic hierarchy and personalization is realized by constructing user community models on the basis of usage data. In contrast to most of the work on Web usage mining, the usage data that are analyzed here correspond to user navigation throughout the Web, rather than a particular Web site, exhibiting as a result a high degree of thematic diversity. For modeling the user communities, we introduce a novel methodology that combines the users’ browsing behavior with thematic information from the Web directories. Following this methodology we enhance the clustering and probabilistic approaches presented in previous work and we also present a new algorithm that combines these two approaches. The resulting community models take the form of Community Web Directories. The proposed personalization methodology is evaluated both on a specialized artificial and a general-purpose Web directory, indicating its potential value to the Web user. The experiments also assess the effectiveness of the different machine learning techniques on the task.

Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information. Value uncertainty arises in many applications during the data collection process. Example sources of uncertainty include measurement/quantisation errors, data staleness, and multiple repeated measurements. With uncertainty, the value of a data item is often represented not by one single value, but by multiple values forming a probability distribution. Rather than abstracting uncertain data by statistical derivatives (such as mean and median), we discover that the accuracy of a decision tree classifier can be much improved if the “complete information” of a data item that takes into account the probability density function (pdf) of that item’s value is utilised. We extend classical decision tree building algorithms to handle data tuples with uncertain values. Extensive experiments have been conducted that show that the resulting classifiers are more accurate than those using value averages. Since processing pdf’s is computationally more costly than processing single values (e.g., averages), decision tree construction on uncertain data is more CPU demanding than that for certain data. To tackle this problem, we propose a series of pruning techniques that can greatly improve construction efficiency.

Given a set of objects and their pairwise distances, we wish to determine a visual representation of the data. We use the quartet paradigm to compute a hierarchy of clusters of the objects. The method is based on an NP-hard graph optimization problem called the Minimum Quartet Tree Cost problem. This paper presents and compares several heuristic approaches to approximate the optimal hierarchy. The performance of the algorithms is tested through extensive computational experiments and it is shown that the Reduced Variable Neighbourhood Search heuristic is the most effective approach to the problem, obtaining high quality solutions in short computational running times.

t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.

Visual methods have been widely studied and used in data cluster analysis, \textit{e.g.}, the VAT algorithm for visual analysis of cluster tendency. Given a pairwise dissimilarity matrix $\bm{D}$ of a set of $n$ objects, methods such as VAT generally represent $\bm{D}$ as an $n\times n$ image $\mathrm{I}(\tilde{\bm{D}})$ where the objects are reordered to highlight cluster structure as dark blocks along the diagonal of the image. A major limitation of such visual methods is their inability to highlight cluster structure in $\mathrm{I}(\tilde{\bm{D}})$ when $\bm{D}$ contains clusters with highly complex structure. In this paper, we address this limitation by proposing a Spectral VAT algorithm, where $\bm{D}$ is mapped to $\bm{D’}$ in an embedding space by spectral decomposition of the Laplacian matrix, and then reordered to $\bm{\tilde{D’}}$ using the VAT algorithm. We propose a strategy to automatically determine the number of clusters in $\mathrm{I}(\bm{\tilde{D’}})$, as well as a visual method for cluster formation from $\mathrm{I}(\bm{\tilde{D’}})$ based on the difference between diagonal blocks and off-diagonal blocks. In addition, we propose a sampling-based extended scheme to enable visual cluster tendency assessment and data partitioning for large data sets. Extensive experimental results on several synthetic and real-world data sets demonstrate the effectiveness of our algorithms.

Long time-series datasets are common in many domains, especially scientific domains. Applications in these fields often require comparing trajectories using similarity measures. Existing methods perform well for short time-series but their evaluation cost degrades rapidly for longer time-series. In this work, we develop a new time-series similarity measure called the Dictionary Compression Score (DCS) for determining time-series similarity. We also show that this method allows us to accurately and quickly calculate similarity for both short and long time-series. We use the well known Kolmogorov Complexity in information theory and the Lempel-Ziv compression framework as a basis to calculate similarity scores. We show that off-the-shelf compressors do not fair well for computing time-series similarity. To address this problem, we developed a novel dictionary-based compression technique to compute time-series similarity. We also develop heuristics to automatically identify suitable parameters for our method, thus removing the task of parameter tuning found in other existing methods. We have extensively compared DCS with existing similarity methods for classification. Our experimental evaluation shows that for long time-series datasets, DCS is accurate, and it is also significantly faster than existing methods.