Oscar Pedreira

Gamification in software engineering – A systematic mapping

Abstract Context Gamification seeks for improvement of the user’s engagement, motivation, and performance when carrying out a certain task, by means of incorporating game mechanics and elements, thus making that task more attractive. Much research work has studied the application of gamification in software engineering for increasing the engagement and results of developers. Objective The objective of this paper is to carry out a systematic mapping of the field of gamification in software engineering in an attempt to characterize the state of the art of this field identifying gaps and opportunities for further research. Method We carried out a systematic mapping with a view to finding the primary studies in the existing literature, which were later classified and analyzed according to four criteria: the software process area addressed, the gamification elements used, the type of research method followed, and the type of forum in which they were published. A subjective evaluation of the studies was also carried out to evaluate them in terms of methodology, empirical evidence, integration with the organization, and replicability. Results As a result of the systematic mapping we found 29 primary studies, published between January 2011 and June 2014. Most of them focus on software development, and to a lesser extent, requirements, project management, and other support areas. In the main, they consider very simple gamification mechanics such as points and badges, and few provide empirical evidence of the impact of gamification. Conclusions Existing research in the field is quite preliminary, and more research effort analyzing the impact of gamification in SE would be needed. Future research work should look at other game mechanics in addition to the basic ones and should tackle software process areas that have not been fully studied, such as requirements, project management, maintenance, or testing. Most studies share a lack of methodological support that would make their proposals replicable in other settings. The integration of gamification with an organization’s existing tools is also an important challenge that needs to be taken up in this field.

Similarity Search Using Sparse Pivots for Efficient Multimedia Information Retrieval

Similarity search is a fundamental operation for applications that deal with unstructured data sources. In this paper we propose a new pivot-based method for similarity search, called sparse spatial selection (SSS). This method guarantees a good pivot selection more efficiently than other methods previously proposed. In addition, SSS adapts itself to the dimensionality of the metric space we are working with, and it is not necessary to specify in advance the number of pivots to extract. Furthermore, SSS is dynamic, it supports object insertions in the database efficiently, it can work with both continuous and discrete distance functions, and it is suitable for secondary memory storage. In this work we provide experimental results that confirm the advantages of the method with several vector and metric spaces

Spatial Selection of Sparse Pivots for Similarity Search in Metric Spaces

Similarity search is a fundamental operation for applications that deal with unstructured data sources. In this paper we propose a new pivot-based method for similarity search, called Sparse Spatial Selection (SSS). The main characteristic of this method is that it guarantees a good pivot selection more efficiently than other methods previously proposed. In addition, SSS adapts itself to the dimensionality of the metric space we are working with, without being necessary to specify in advance the number of pivots to use. Furthermore, SSS is dynamic, that is, it is capable to support object insertions in the database efficiently, it can work with both continuous and discrete distance functions, and it is suitable for secondary memory storage. In this work we provide experimental results that confirm the advantages of the method with several vector and metric spaces. We also show that the efficiency of our proposal is similar to that of other existing ones over vector spaces, although it is better over general metric spaces.

A Dynamic Pivot Selection Technique for Similarity Search

All pivot-based algorithms for similarity search use a set of reference points called pivots. The pivot-based search algorithm precomputes some distances to these reference points, which are used to discard objects during a search without comparing them directly with the query. Though most of the algorithms proposed to date select these reference points at random, previous works have shown the importance of intelligently selecting these points for the index performance. However, the proposed pivot selection techniques need to know beforehand the complete database to obtain good results, which inevitably makes the index static. More recent works have addressed this problem, proposing techniques that dynamically select pivots as the database grows. This paper presents a new technique for choosing pivots, that combines the good properties of previous proposals with the recently proposed dynamic selection. The experimental evaluation provided in this paper shows that the new proposed technique outperforms the state-of-art methods for selecting pivots.

Compact Querieable Representations of Raster Data

In Geographic Information Systems (GIS) the attributes of the space (altitude, temperature, etc.) are usually represented using a raster model. There are no compact representations of raster data that provide efficient query capabilities. In this paper we propose compact representations to efficiently store and query raster datasets in main memory. We experimentally compare our proposals with traditional storage mechanisms for raster data, showing that our structures obtain competitive space performance while efficiently answering range queries involving the values stored in the raster.

Clustering-based similarity search in metric spaces with sparse spatial centers

Metric spaces are a very active research field which offers efficient methods for indexing and searching by similarity in large data sets. In this paper we present a new clustering-based method for similarity search called SSSTree. Its main characteristic is that the centers of each cluster are selected using Sparse Spatial Selection (SSS), a technique initially developed for the selection of pivots. SSS is able to adapt the set of selected points (pivots or cluster centers) to the intrinsic dimensionality of the space. Using SSS, the number of clusters in each node of the tree depends on the complexity of the subspace it represents. The space partition in each node will be made depending on that complexity, improving thus the performance of the search operation. In this paper we present this new method and provide experimental results showing that SSSTree performs better than previously proposed indexes.

Optimal Pivots to Minimize the Index Size for Metric Access Methods

We consider the problem of similarity search in metric spaces with costly distance functions and large databases. There is a trade-off between the amount of information stored in the index and the reduction in the number of comparisons for solving a query. Pivot-based methods clearly outperform clustering-based ones in number of comparisons, but their space requirements are higher and this can prevent their application in real problems. Therefore, several strategies have been proposed that reduce the space needed by pivot-based methods, as BAESA, FQA or KVP. In this paper, we analyze the usefulness of pivots depending on their proximity to the object. As consequence of this analysis, we propose a new pivot-based method that requires an amount of space equal or very close to that needed by clustering-based methods. We provide experimental results that show that our proposal represents a competitive strategy to clustering oriented solutions when using the same amount of memory.

A compact representation of graph databases

Graph databases have emerged as an alternative data model with applications in many complex domains. Typically, the problems to be solved in such domains involve managing and mining huge graphs. The need for efficient processing in such applications has motivated the development of methods for graph compression and indexing. However, most methods aim at an efficient representation and processing of simple graphs (without attributes in nodes or edges, or multiple edges for a given pair of nodes). In this paper we present a model for compact representation of general graph databases. It represents an attractive alternative due to the compression rates it achieves and its efficient navigation operations.

Exploiting SIMD instructions in current processors to improve classical string algorithms

Current processors include instruction set extensions especially designed for improving the performance of media, imaging, and 3D workloads. These instructions are rarely considered when implementing practical solutions for algorithms and compressed data structures, mostly because they are not directly generated by the compiler. In this paper, we proclaim their benefits and encourage their use, as they are an unused asset included in almost all general-purpose computers. As a proof of concept, we perform an experimental evaluation by straightforwardly including some of these complex instructions in basic string algorithms used for indexing and search, obtaining significant speedups. This opens a new interesting line of research: designing new algorithms and data structures by taking into account the existence of these sets of instructions, in order to achieve significant speedups at no extra cost.

An Ontology-Based Index to Retrieve Documents with Geographic Information

Both Geographic Information Systemsand Information Retrievalhave been very active research fields in the last decades. Lately, a new research field called Geographic Information Retrievalhas appeared from the intersection of these two fields. The main goal of this field is to define index structures and techniques to efficiently store and retrieve documents using both the text and the geographic references contained within the text. We present in this paper a new index structure that combines an inverted index, a spatial index, and an ontology-based structure. This structure improves the query capabilities of other proposals. In addition, we describe the architecture of a system for geographic information retrieval that uses this new index structure. This architecture defines a workflow for the extraction of the geographic references in the document.