Sonifying Git History

Version control is a technique that software developers use in industry to manage their source code artifacts. One benefit of using version control is that it produces a history of every change made to a codebase, which developers frequently analyze in order to aid the software development process. However, version control history contains highly multidimensional and temporal data. State of the art techniques can show several of these dimensions, but they cannot show a large number of dimensions simultaneously without becoming difficult to understand. An alternative technique to understand temporal data with high dimensionality is sonification. Sonification maps information to sound. In this thesis we propose the use of earcons and parameter mapping sonification to show version control history. Using sonification, we can show more dimensions of version history simultaneously than other state of the art techniques. Our first technique, GitSonifier, uses only sonification to portray version history and historical conflict data. A user study shows that developers can easily understand the sonification, but we also find limitations where visualization may be preferred. Our second technique, GitVS, uses a combination of both visualization and sonification to overcome these limitations. Advisors: Myra B. Cohen and Anita Sarm

Sonifying Git History

Version control is a technique that software developers use in industry to manage their source code artifacts. One benefit of using version control is that it produces a history of every change made to a codebase, which developers frequently analyze in order to aid the software development process. However, version control history contains highly multidimensional and temporal data. State of the art techniques can show several of these dimensions, but they cannot show a large number of dimensions simultaneously without becoming difficult to understand. An alternative technique to understand temporal data with high dimensionality is sonification. Sonification maps information to sound. In this thesis we propose the use of earcons and parameter mapping sonification to show version control history. Using sonification, we can show more dimensions of version history simultaneously than other state of the art techniques. Our first technique, GitSonifier, uses only sonification to portray version history and historical conflict data. A user study shows that developers can easily understand the sonification, but we also find limitations where visualization may be preferred. Our second technique, GitVS, uses a combination of both visualization and sonification to overcome these limitations. Advisors: Myra B. Cohen and Anita Sarm

Unconventional computation and teaching: Proposal for MUSIC, a tone-based scripting language for accessibility, computation and education

This paper provides a proposal for a tone-based programming/scripting language called MUSIC (the name is an acronym for Music-Utilizing Script Input Code). In a MUSIC program input and output consists entirely of musical tones. Computation can be done through musical transformations of notes and melodies. MUSIC can be used for teaching the basics of script-based programming, computer-aided composition, and provided programming access to those with limitations in sight or physical accessibility. As a result of MUSIC's approach to tone-based programming and computation, it also allows for a development environment that utilizes computer expressive performance for highlighting structure, and emotional transformation to highlight bugs. © 2014 Old City Publishing, Inc

Towards harmonic extensions of pulsed melodic affective processing - further musical structures for increasing transparency in emotional computation

Pulsed Melodic Affective Processing (PMAP) is a method for the processing of artificial emotions in affective computing. PMAP is a data stream which can be listened to, as well as computed with. The affective state is represented by numbers which are analogues of musical features, rather than by a binary stream. Previous affective computation has been done with emotion category indices, or real numbers representing positivity of emotion, etc. PMAP data can be generated directly by sound and rhythms (e.g. heart rates or key-press speeds) and turned directly into into music with minimal transformation. This is because PMAP data is music and computations done with PMAP data are computations done with music. Why is this important? Because PMAP is constructed so that the emotion which its data represents at the computational level, will be similar to the emotion which a person "listening" to the PMAP melody hears. So PMAP can be used to calculate "feelings" and the result data will "sound like" the feelings calculated. Harmonic PMAP (PMAPh) is an extension of PMAP allowing harmonies to be used in calculations © 2014 Old City Publishing, Inc

A Content Analysis-Based Approach to Explore Simulation Verification and Identify Its Current Challenges

Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a corpus of 4,047 peer-reviewed Modeling and Simulation (M&S) publications dealing with a wide range of studies of simulation verification from 1963 to 2015. We group the selected papers by decade of publication to provide insights and explore the corpus from four perspectives: (i) the positioning of prominent concepts across the corpus as a whole; (ii) a comparison of the prominence of verification, validation, and Verification and Validation (V&V) as separate concepts; (iii) the positioning of the concepts specifically associated with verification; and (iv) an evaluation of verification\u27s defining characteristics within each decade. Our analysis reveals unique characterizations of verification in each decade. The insights gathered helped to identify and discuss three categories of verification challenges as avenues of future research, awareness, and understanding for researchers, students, and practitioners. These categories include conveying confidence and maintaining ease of use; techniques\u27 coverage abilities for handling increasing simulation complexities; and new ways to provide error feedback to model users

Pulsed Melodic Affective Processing: Musical structures for increasing transparency in emotional computation

Pulsed Melodic Affective Processing (PMAP) is a method for the processing of artificial emotions in affective computing. PMAP is a data stream designed to be listened to, as well as computed with. The affective state is represented by numbers that are analogues of musical features, rather than by a binary stream. Previous affective computation has been done with emotion category indices, or real numbers representing various emotional dimensions. PMAP data can be generated directly by sound (e.g. heart rates or key-press speeds) and turned directly into music with minimal transformation. This is because PMAP data is music and computations done with PMAP data are computations done with music. This is important because PMAP is constructed so that the emotion that its data represents at the computational level will be similar to the emotion that a person “listening” to the PMAP melody hears. Thus, PMAP can be used to calculate “feelings” and the result data will “sound like” the feelings calculated. PMAP can be compared to neural spike streams, but ones in which pulse heights and rates encode affective information. This paper illustrates PMAP in a range of simulations. In a multi-agent simulation, initial results support that an affective multi-robot security system could use PMAP to provide a basic control mechanism for “search-and-destroy”. Results of fitting a musical neural network with gradient descent to help solve a text emotional detection problem are also presented. The paper concludes by discussing how PMAP may be applicable in the stock markets, using a simplified order book simulation. © 2014, The Society for Modeling and Simulation International. All rights reserved