Knowledge discovery in software teams by means of evolutionary visual software analytics

González-Torres, Antonio; García-Peñalvo, Francisco José; Therón-Sánchez, Roberto; Colomo‐Palacios, Ricardo

doi:10.1016/j.scico.2015.09.005

Cited by 17 publications

(8 citation statements)

References 19 publications

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“…Therefore, González-Torres described the process of applying visual analytics to software evolution to enhance the understanding of changes with the active participation of users by means of human-computer interaction and implemented Maleku, a proof of concept architecture (A. Gonzalez-Torres, 2015; A. González-Torres et al, 2016). Visual analytics is the combination of interactive visualizations with analysis techniques to facilitate the decision-making processes (Keim, Kohlhammer, Ellis, & Mansmann, 2010).…”

Section: Previous Workmentioning

confidence: 99%

“…Maleku was designed to support both programmers and managers when correlating metrics, project structure, inheritance, interface implementation and socio-technical relationships (A. González-Torres et al, 2013;A. González-Torres et al, 2016).…”

Section: Previous Workmentioning

confidence: 99%

“…Information visualization frequently is referred as visual analytics, however, there exist several differences between both. Visual analytics makes intensive use of data analysis, coordinated and multiple views and combines the advantages of machines with human strengths, such as analysis, intuition, problem solving and visual perception (A. González-Torres et al, 2016). Therefore, it offers the potential to explore different levels of detail using multiple visual representations, coordinated together and supported by the use of interaction techniques (North & Shneiderman, 2000).…”

Section: Rq6mentioning

confidence: 99%

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A visual analytics architecture for the analysis and understanding of software systems

et al. 2019

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Visual analytics facilitates the creation of knowledge to interpret trends and relationships for better decision making. However, it has not being used widely for the understanding of software systems and the change process that takes place during their development and maintenance. This occurs despite the need of project managers and developers to analyze their systems to calculate the complexity, cohesion, direct, indirect and logical coupling, detect clones, defects and bad smells, and the comparison of individual revisions. This research considers the design of an extensible and scalable architecture to incorporate new and existing methods to retrieve source code from different versioning systems, to carry out the analysis of programs in different languages, to perform the calculation of software metrics and to present the results using visual representations, incorporated as Eclipse and Visual Studio extensions. Consequently, the aim of this work is to design a visual analytics architecture for the analysis and understanding of systems in different languages and its main contributions are the specification of the design and requirements of such architecture, taking as base the lessons learned in Maleku (A. González-Torres et al., 2016).

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Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Rq6mentioning

confidence: 99%

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A visual analytics architecture for the analysis and understanding of software systems

et al. 2019

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“…User reports were positive. Another application of visual analytics in software engineering has been reported in the area of management of human resources during the development and maintenance of a software system [42]. Software development projects often are beset by problems associated with rotation and distributed software development.…”

Section: Visual Analytics For Software Engineeringmentioning

confidence: 99%

Big Data and software engineering: prospects for mutual enrichment

Arndt

2017

Iran J Comput Sci

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Software engineering has evolved over the last 50 years, initially as a response to the so-called software crisis (the problems that organizations had producing quality software systems on time and on budget) of the 1960s and 1970s. Software engineering (SE) has been defined as "the application of a systematic, disciplined, quantifiable approach to the development, operation, and maintenance of software; that is, the application of engineering to software". Software engineering has developed a number of approaches to areas such as software requirements, software design, software testing, and software maintenance. Software development processes such as the waterfall model, incremental development, and the spiral model have been successfully applied to produce high-quality software on time and under budget. More recently, agile software development has gained popularity as an alternative to the more traditional development methods for development of complex systems. Within the last decade or so, advances in technologies such as mobile computing, social networks, cloud computing, and the Internet of things have given rise to massive datasets which have been given the name Big Data (BD). Big Data has been defined as data with 3Vs-high volume, velocity, and variety. Big Data contains so much data that low probability events are captured in the data. These events can be discovered using analytics methods and turned into actionable intelligence which can be used by businesses to gain a competitive advantage. Unfortunately, the very scale of BD often renders inadequate SQL-based relational database systems which have formed the backbone of data intensive systems for the last 30 years, requiring new NoSQL technologies to be effective. In this paper, we will explore how well-established SE technology can be adapted to support successful development of BD projects, as well as how BD techniques can be used to increase the utility of SE processes and techniques. Thus, BD and SE may mutually support and enrich each other.

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“…The term big data is used frequently to refer to the administration of large amounts of data to facilitate informed decision making, according to the needs and requirements of organizations [9]. In this context, this work considers that the visualization of information is a critical element in the analysis process because it takes advantage of the visual and cognitive abilities of people to explore, discover, interpret and understand patterns in the data through the use of visual representations and human-computer interaction techniques [29,11].…”

Section: Introductionmentioning

confidence: 99%

A lightweight environment for 2D visual applications

Arce-Orozco

González-Torres

Mata‐Montero

EPiC Series in Computing

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People and organizations constantly use a wide variety of devices and formats to produce large volumes of data. This involves a series of challenges related to processing and transforming data into valuable knowledge to carry out informed decisions. This research work departs from the fact that information visualization is a critical element in the pro- cess of data analysis that takes advantage of the visual and cognitive abilities of people to explore, discover, interpret, and understand patterns in data with the use of visual representations and human-computer interaction techniques. This research proposes Dio ̈ko ̈l, a programming environment developed with Lua and OpenVG to facilitate the learning process of programmers with little experience in the implementation of visualizations. The environment was designed after the careful study of similar tools and the most popular visualization libraries. Its design takes into account their weaknesses and strengths to propose a set of features that can make it an efficient alternative to learning about and program visualizations. In addition to typical functionality provided by several visualization tools and libraries, Dio ̈k ̈ol is a lightweight environment that provides a simple and effective event manager, is scalable, small, portable and contributes an environment with a simple and easy to understand graphical interface and functionality, similar to the ones provided by Processing.

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Knowledge discovery in software teams by means of evolutionary visual software analytics

Cited by 17 publications

References 19 publications

A visual analytics architecture for the analysis and understanding of software systems

A visual analytics architecture for the analysis and understanding of software systems

Big Data and software engineering: prospects for mutual enrichment

A lightweight environment for 2D visual applications

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