Programmers commonly search the Web to find code examples that can help them solve a specific programming task. While some novice programmers may be willing to spend as much time as needed to understand a found code example, more experienced ones want to spend as little time as possible. They want to get a quick overview of the example's operation, so they can start working with it immediately. Getting this overview is often non-trivial and requires a tedious and manual inspection process. In this paper, we introduce a technique called Multi-staging to Understand, which streamlines this inspection process by distilling the essence of code examples. The essence of a code example conveys the most important aspects of the example's intended function. Our technique automatically decomposes the code in an example into code stages that can be explored non-sequentially; enabling fast exploratory learning. We discuss the key components of our technique and describe empirical results based on actual code examples on StackOverflow.

The past few years have witnessed the rise of software question and answer sites like StackOverflow, where developers can pose detailed coding questions and receive quality answers. Developers using these sites engage in a complex code foraging process of understanding and adapting the code snippets they encounter. We introduce the notion of source code curation to cover the act of discovering some source code of interest, cleaning and transforming (refining) it, and then presenting it in a meaningful and organized way. In this paper, we present Vesperin, a source code curation system geared towards curating Java code examples on StackOverflow.

This paper sketches a research path that seeks to examine the search for suitable code problem, based on the observation that when code retargeting is included within a code search activity, developers can justify the suitability of these results upfront and thus reduce their searching efforts looking for suitable code. To support this observation, this paper introduces the Snippet Retargeting Approach, or simply SNIPR. SNIPR complements code search with code retargeting capabilities. These capabilities' intent is to help expedite the process of determining if a found example is a best fit. They do that by allowing developers to explore code modification ideas in place, without requiring to leave the search interface. With SNIPR, developers engage in a virtuous loop where they find code, retarget code, and select only code choices they can justify as suitable. This assures immediate feedback on retargeted examples and thus saves valuable time searching for appropriate code.

Index tuning; i.e., selecting indexes that are appropriate for the workload to obtain good system performance, is a crucial task for database administrators. Administrators rely on automated index advisors for this task, but existing advisors work either offline, requiring a-priori knowledge of the workload, or online, taking the administrator out of the picture and assuming total control of the index tuning task. Semi-automatic index tuning is a new paradigm that achieves a middle ground: the advisor analyzes the workload online and provides recommendations tailored to the current workload, and the administrator is able to provide feedback to refine future recommendations. In this demonstration we present Kaizen, an index tuning tool that implements semi-automatic tuning.

Abstract. Branding is a concept that spans multiple areas of application. It is used to create identifiable associations between any type of entity and its brands, with the sole purpose of differentiating it from its peers. This paper introduces the Branding analysis pattern. This pattern focuses on providing a conceptual model that represents any type of branding need in any domain. To achieve this, we used Software Stability Models “SSMs” [1, 2, 3, 4] as the main recipe. This paper also provides full pattern documentation. Additionally, in order to demonstrate the application of this pattern, we provide two examples: one related to HCI, and the other one related to Marketing. Keywords: Branding, Branding Analysis Patterns, Software Stability Models. I. Introduction Branding is a mental construct that establishes a connection between your desires and a particular entity. This connection is achieved through the use of a combination of logos, names, trademarks, tag lines, color palettes, sounds and tones, and physical and behavioral features in a manner that reflects the emotional make-up of any entity. Branding’s origin references to the earliest shepherds that branded their cattle to identify them and differentiate them from other shepherds’ cattle. Nowadays, this activity has found great acceptance in areas as diverse as Marketing and Forensics. In Marketing, branding has emerged as the means for both enhancing customers’ perception with respect to an identifiable product, establishing a pervasive means to communicate information and mark differences among this product and its peers, i.e. Nike,