Nieves R. Brisaboa

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.

Lightweight natural language text compression

Variants of Huffman codes where words are taken as the source symbols are currently the most attractive choices to compress natural language text databases. In particular, Tagged Huffman Code by Moura et al. offers fast direct searching on the compressed text and random access capabilities, in exchange for producing around 11% larger compressed files. This work describes End-Tagged Dense Code and (s, c)-Dense Code, two new semistatic statistical methods for compressing natural language texts. These techniques permit simpler and faster encoding and obtain better compression ratios than Tagged Huffman Code, while maintaining its fast direct search and random access capabilities. We show that Dense Codes improve Tagged Huffman Code compression ratio by about 10%, reaching only 0.6% overhead over the optimal Huffman compression ratio. Being simpler, Dense Codes are generated 45% to 60% faster than Huffman codes. This makes Dense Codes a very attractive alternative to Huffman code variants for various reasons: they are simpler to program, faster to build, of almost optimal size, and as fast and easy to search as the best Huffman variants, which are not so close to the optimal size.

(S,C)-Dense Coding: An Optimized Compression Code for Natural Language Text Databases

This work presents (s,c)-Dense Code, a new method for compressing natural language texts. This technique is a generalization of a previous compression technique called End-Tagged Dense Code that obtains better compression ratio as well as a simpler and faster encoding than Tagged Huffman. At the same time, (s,c)-Dense Code is a prefix code that maintains the most interesting features of Tagged Huffman Code with respect to direct search on the compressed text. (s,c)-Dense Coding retains all the efficiency and simplicity of Tagged Huffman, and improves its compression ratios.

An efficient compression code for text databases

We present a new compression format for natural language texts, allowing both exact and approximate search without decompression. This new code -called End-Tagged Dense Code- has some advantages with respect to other compression techniques with similar features such as the Tagged Huffman Code of [Moura et al., ACM TOIS 2000]. Our compression method obtains (i) better compression ratios, (ii) a simpler vocabulary representation, and (iii) a simpler and faster encoding. At the same time, it retains the most interesting features of the method based on the Tagged Huffman Code, i.e., exact search for words and phrases directly on the compressed text using any known sequential pattern matching algorithm, efficient word-based approximate and extended searches without any decoding, and efficient decompression of arbitrary portions of the text. As a side effect, our analytical results give new upper and lower bounds for the redundancy of d-ary Huffman codes.

k2-Trees for Compact Web Graph Representation

This paper presents a Web graph representation based on a compact tree structure that takes advantage of large empty areas of the adjacency matrix of the graph. Our results show that our method is competitive with the best alternatives in the literature, offering a very good compression ratio (3.3---5.3 bits per link) while permitting fast navigation on the graph to obtain direct as well as reverse neighbors (2---15 microseconds per neighbor delivered). Moreover, it allows for extended functionality not usually considered in compressed graph representations.

Word-based self-indexes for natural language text

The inverted index supports efficient full-text searches on natural language text collections. It requires some extra space over the compressed text that can be traded for search speed. It is usually fast for single-word searches, yet phrase searches require more expensive intersections. In this article we introduce a different kind of index. It replaces the text using essentially the same space required by the compressed text alone (compression ratio around 35%). Within this space it supports not only decompression of arbitrary passages, but efficient word and phrase searches. Searches are orders of magnitude faster than those over inverted indexes when looking for phrases, and still faster on single-word searches when little space is available. Our new indexes are particularly fast at counting the occurrences of words or phrases. This is useful for computing relevance of words or phrases. We adapt self-indexes that succeeded in indexing arbitrary strings within compressed space to deal with large alphabets. Natural language texts are then regarded as sequences of words, not characters, to achieve word-based self-indexes. We design an architecture that separates the searchable sequence from its presentation aspects. This permits applying case folding, stemming, removing stopwords, etc. as is usual on inverted indexes.

DACs: Bringing direct access to variable-length codes

We present a new variable-length encoding scheme for sequences of integers, Directly Addressable Codes (DACs), which enables direct access to any element of the encoded sequence without the need of any sampling method. Our proposal is a kind of implicit data structure that introduces synchronism in the encoded sequence without using asymptotically any extra space. We show some experiments demonstrating that the technique is not only simple, but also competitive in time and space with existing solutions in several applications, such as the representation of LCP arrays or high-order entropy-compressed sequences.

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.

Directly Addressable Variable-Length Codes

We introduce a symbol reordering technique that implicitly synchronizes variable-length codes, such that it is possible to directly access the i -th codeword without need of any sampling method. The technique is practical and has many applications to the representation of ordered sets, sparse bitmaps, partial sums, and compressed data structures for suffix trees, arrays, and inverted indexes, to name just a few. We show experimentally that the technique offers a competitive alternative to other data structures that handle this problem.