Consumers can realize significant cost savings by procuring resources from computational spot markets such as Amazon Elastic Compute Cloud (EC2) Spot Instances. They can take advantage of the price differentials across time slots, regions, and instance types to minimize the total cost of running their applications on the cloud. However, Spot markets are inherently volatile and dynamic, as a consequence of which Spot prices change continuously. As such, prospective bidders can benefit from intelligent insights into the Spot market dynamics that can help them make more informed bidding decisions. To enable this, we propose a descriptive statistics approach for the analysis of Amazon EC2 Spot markets to detect typical pricing patterns including the presence of seasonal components, extremes and trends. We use three statistical measures - the Gini coefficient, the Theil index, and the exponential weighted moving average. We also devise a model for estimating minimum bids such that the Spot instances will run for specified durations with a probability greater than a set value based on different look back periods. Experimental results show that our estimation yields on average a bidding strategy that can reliably secure an instance at least 80% of the time at minimum target guarantee between 50% and 95%.

Formal methods offer an effective means to assert the correctness of software systems through mathematical reasoning. However, the need to formulate system properties in a purely mathematical fashion can create pragmatic barriers to the application of these techniques. For this reason, Dwyer et al. invented property specification patterns which is a system of recurring solutions to deal with the temporal intricacies that would make the construction of reactive systems very hard otherwise. Today, property specification patterns provide general rules that help practitioners to qualify order and occurrence, to quantify time bounds, and to express probabilities of events. Nevertheless, a comprehensive framework combining qualitative, real-time, and probabilistic property specification patterns has remained elusive. The benefits of such a framework are twofold. First, it would remove the distinction between qualitative and quantitative aspects of events; and second, it would provide a structure to systematically discover new property specification patterns. In this paper, we report on such a framework and present a unified catalogue that combines all known plus 40 newly identified or extended patterns. We also offer a natural language front-end to map patterns to a temporal logic of choice. To demonstrate the virtue of this new framework, we applied it to a variety of industrial requirements, and use PSPWizard, a tool specifically developed to work with our unified pattern catalogue, to automatically render concrete instances of property specification patterns to formulae of an underlying temporal logic of choice.

Requirements engineering (RE) is a coordinated effort to allow clients, users, and software engineers to jointly formulate assumptions, constraints, and goals about a software solution. However, one of the most challenging aspects of RE is the detection of inconsistencies between requirements. To address this issue, we have developed REInDetector, a knowledge-based requirements engineering tool, supporting automatic detection of a range of inconsistencies. It provides facilities to elicit, structure, and manage requirements with distinguished capabilities for capturing the domain knowledge and the semantics of requirements. This permits an automatic analysis of both consistency and realizability of requirements. REInDetector finds implicit consequences of explicit requirements and offers all stakeholders an additional means to identify problems in a more timely fashion than existing RE tools. In this paper, we describe the Description Logic used to capture requirements, the REInDetector tool, its support for inconsistency detection, and its efficacy as applied to several RE examples. An important feature of REInDetector is also its ability to generate comprehensive explanations to provide more insights into the detected inconsistencies.

Recent research has shown the value of social metrics for defect prediction. Yet many repositories lack the information required for a social analysis. So, what other means exist to infer how developers interact around their code? One option is static code metrics that have already demonstrated their usefulness in analyzing change in evolving software systems. But do they also help in defect prediction? To address this question we selected a set of static code metrics to determine what classes are most "active" (i.e., the classes where the developers spend much time interacting with each other's design and implementation decisions) in 33 open-source Java systems that lack details about individual developers. In particular, we assessed the merit of these activity-centric measures in the context of "inspection optimization" — a technique that allows for reading the fewest lines of code in order to find the most defects. For the task of inspection optimization these activity measures perform as well as (usually, within 4%) a theoretical upper bound on the performance of any set of measures. As a result, we argue that activity-centric static code metrics are an excellent predictor for defects.

We are concerned with analyzing software, in particular, with its nature and how developer decisions and behavior impact the quality of the product they produce. This is the domain of empirical software engineering where measurement seeks to capture attributes affecting the product, process, and resources of software development. A well-established means to study software attributes is metrics data mining. But, even though a variety of frameworks have emerged that can distill desired measures from software systems (e.g., JHawk or SonarJ), a systematic approach to collecting measures from large data sets has still eluded us. For this reason, we have developed the Java Code Tomograph (jCT), a novel framework for metrics extraction and processing. jCT offers an extensible measurement infrastructure with built-in support for the curated repositories Qualitas Corpus and Helix. With jCT, large-scale empirical studies of code within the same software system or across different software systems become feasible. In this paper, we provide an overview of jCT's main design features and discuss its operation in relation to the effectiveness of the framework.

Model checking provides a powerful means to assert and verify desired system properties. But, for the verification process to become feasible, a correct formulation of these properties in a temporal logic is necessary - a potential barrier to application in practice. Research on property specification has supplied us with rich pattern catalogs that capture commonly occurring system properties in different temporal logics. Furthermore, these property specification pattern catalogs usually offer both a structured English grammar to facilitate the pattern selection and an associated template solutions to express the properties formally. Yet, the actual use of property specification patterns remains cumbersome, due to limited tool support. For this reason, we have developed the Property Specification Pattern Wizard (PSPWizard), a framework that defines an interface for the currently accepted property specification pattern libraries. PSPWizard consists of two main building blocks: a mapping generator that weaves a given pattern library with a target logic and a GUI front-end to the structured English grammar tailored to those patterns that are supported in the target logic.

In order to increase our ability to use measurement to support software development practise we need to do more analysis of code. However, empirical studies of code are expensive and their results are difficult to compare. We describe the Qualitas Corpus, a large curated collection of open source Java systems. The corpus reduces the cost of performing large empirical studies of code and supports comparison of measurements of the same artifacts. We discuss its design, organisation, and issues associated with its development.

Software metrics offer us the promise of distilling useful information from vast amounts of software in order to track development progress, to gain insights into the nature of the software, and to identify potential problems. Unfortunately, however, many software metrics exhibit highly skewed, non-Gaussian distributions. As a consequence, usual ways of interpreting these metrics - for example, in terms of ldquoaveragerdquo values - can be highly misleading. Many metrics, it turns out, are distributed like wealth - with high concentrations of values in selected locations. We propose to analyze software metrics using the Gini coefficient, a higher-order statistic widely used in economics to study the distribution of wealth. Our approach allows us not only to observe changes in software systems efficiently, but also to assess project risks and monitor the development process itself. We apply the Gini coefficient to numerous metrics over a range of software projects, and we show that many metrics not only display remarkably high Gini values, but that these values are remarkably consistent as a project evolves over time.