Static and Dynamic Software Quality Metric Tools

The ability to detect and predict poor software quality is of major importance to software engineers, managers, and quality assurance organizations. Poor software quality leads to increased development costs and expensive maintenance. With so much attention on exacerbated budgetary constraints, a viable alternative is necessary. Software quality metrics are designed for this purpose. Metrics measure aspects of code or PDL representations, and can be collected and used throughout the life cycle [RAMC85]

A Grassroots Approach to Graduate Teaching Assistant Mentoring

Graduate students, whether master's or doctoral candidates, benefit greatly from their academic experiences. However, graduate school is not limited to course work and research, but it also includes teaching experiences as graduate teaching assistants (GTAs). Although GTAs are technically proficient in course materials, other factors can cause teaching experiences to go awry for them, their students, or the course supervisor. These factors arise out of a need for quality training on issues including pedagogy, interaction resolution, organizational concerns, and professional matters. This paper provides a grassroots approach to improve teachine techniques through GTA mentoring. GTAs are encouraged, with materials supplied here, to seek out and consult with more experienced GTAs who will serve as their mentors

A Model Based on Software Quality Factors which Predicts Maintainability

Computer scientists are continually attempting to improve software system development. Systems are developed in a top-down fashion for better modularity and understandability. Performance enhancements are implemented for more speed. One area in which a great deal of effort is being devoted is software maintenance. Brooks estimates that fifty percent of the development cost of a software system is for maintenance activities [BROF82]. Since a large portion of the effort of a system is devoted to maintenance, it is reasonable to assume that driving down maintenance costs would drive down the overall cost of the system. Measuring the complexity of a software system could aid in this attempt. By lowering the complexity of the system or of subsystems within the system, it may be possible to reduce the amount of maintenance necessary. Software quality metrics were developed to measure the complexity of software systems. This study relates the complexity of the system as measured by software metrics to the amount of maintenance necessary to that system. We have developed a model which uses several software quality metrics as parameters to predict maintenance activity

The Use of Complexity Metrics Throughout the Software Lifecycle

Software metrics attempt to uncover difficult or complex components of a software system. The hypothesis is that complex components are more difficult to understand, hence they are hard to maintain and more prone to error. Discovery of these complex components can aid the software developer in selecting which components to redesign, direct the testing effort, and indicate the maintenance effort required. Previous studies have demonstrated two main concepts: (1) there exists a high correlation between design complexity and source code complexity, and (2) metrics applied to source code have a high correlation to the maintenance activity needed. This previous research motivates us to develop a methodology which uses complexity metrics throughout the software life cycle. Programmer productivity may be increased and software development cost may be reduced if error prone software is discovered early in the life cycle

A Reliability Model Incorporating Software Quality Factors

In this paper we describe our initial work on a long-term project to develop and validate a reliability model and a new class of software complexity metrics which are related to this model. In contrast to previous "black box" approaches, the reliability model is novel because it incorporates knowledge about the system in the form of quantitative software complexity metrics. While the initial model uses existing software metrics a parallel effort in this project is investigating new classes of metrics, interface and dynamic metrics, which are useful in their own right but are also of particular relevance to the reliability model. The initial definitions of both the model and the metrics are given along with a description of the next research milestones

Modulation of cognitive performance and mood by aromas of peppermint and ylang-ylang

This study provides further evidence for the impact of the aromas of plant essential oils on aspects of cognition and mood in healthy participants. One hundred and forty-four volunteers were randomly assigned to conditions of ylang-ylang aroma, peppermint aroma, or no aroma control. Cognitive performance was assessed using the Cognitive Drug Research computerized assessment battery, with mood scales completed before and after cognitive testing. The analysis of the data revealed significant differences between conditions on a number of the factors underpinning the tests that constitute the battery. Peppermint was found to enhance memory whereas ylang-ylang impaired it, and lengthened processing speed. In terms of subjective mood peppermint increased alertness and ylang-ylang decreased it, but significantly increased calmness. These results provide support for the contention that the aromas of essential oils can produce significant and idiosyncratic effects on both subjective and objective assessments of aspects of human behavior. They are discussed with reference to possible pharmacological and psychological modes of influence

SDSS-IV MaNGA: bulge-disc decomposition of IFU data cubes (BUDDI)

With the availability of large integral field unit (IFU) spectral surveys of nearby galaxies, there is now the potential to extract spectral information from across the bulges and discs of galaxies in a systematic way. This information can address questions such as how these components built up with time, how galaxies evolve and whether their evolution depends on other properties of the galaxy such as its mass or environment. We present bulge–disc decomposition of IFU data cubes (buddi), a new approach to fit the two-dimensional light profiles of galaxies as a function of wavelength to extract the spectral properties of these galaxies’ discs and bulges. The fitting is carried out using galfitm, a modified form of galfit which can fit multiwaveband images simultaneously. The benefit of this technique over traditional multiwaveband fits is that the stellar populations of each component can be constrained using knowledge over the whole image and spectrum available. The decomposition has been developed using commissioning data from the Sloan Digital Sky Survey-IV Mapping Nearby Galaxies at APO (MaNGA) survey with redshifts z 22 arcsec, but can be applied to any IFU data of a nearby galaxy with similar or better spatial resolution and coverage. We present an overview of the fitting process, the results from our tests, and we finish with example stellar population analyses of early-type galaxies from the MaNGA survey to give an indication of the scientific potential of applying bulge–disc decomposition to IFU data

The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey

The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar spectra, along with the data presented in previous data releases. These spectra were obtained with the new BOSS spectrograph and were taken between 2009 December and 2011 July. In addition, the stellar parameters pipeline, which determines radial velocities, surface temperatures, surface gravities, and metallicities of stars, has been updated and refined with improvements in temperature estimates for stars with T_eff<5000 K and in metallicity estimates for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars presented in DR8, including stars from SDSS-I and II, as well as those observed as part of the SDSS-III Sloan Extension for Galactic Understanding and Exploration-2 (SEGUE-2). The astrometry error introduced in the DR8 imaging catalogs has been corrected in the DR9 data products. The next data release for SDSS-III will be in Summer 2013, which will present the first data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) along with another year of data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at http://www.sdss3.org/dr

The Eighth Data Release of the Sloan Digital Sky Survey: First Data from SDSS-III

The Sloan Digital Sky Survey (SDSS) started a new phase in August 2008, with new instrumentation and new surveys focused on Galactic structure and chemical evolution, measurements of the baryon oscillation feature in the clustering of galaxies and the quasar Ly alpha forest, and a radial velocity search for planets around ~8000 stars. This paper describes the first data release of SDSS-III (and the eighth counting from the beginning of the SDSS). The release includes five-band imaging of roughly 5200 deg^2 in the Southern Galactic Cap, bringing the total footprint of the SDSS imaging to 14,555 deg^2, or over a third of the Celestial Sphere. All the imaging data have been reprocessed with an improved sky-subtraction algorithm and a final, self-consistent photometric recalibration and flat-field determination. This release also includes all data from the second phase of the Sloan Extension for Galactic Understanding and Evolution (SEGUE-2), consisting of spectroscopy of approximately 118,000 stars at both high and low Galactic latitudes. All the more than half a million stellar spectra obtained with the SDSS spectrograph have been reprocessed through an improved stellar parameters pipeline, which has better determination of metallicity for high metallicity stars.Comment: Astrophysical Journal Supplements, in press (minor updates from submitted version

The Fourteenth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the extended Baryon Oscillation Spectroscopic Survey and from the second phase of the Apache Point Observatory Galactic Evolution Experiment

The fourth generation of the Sloan Digital Sky Survey (SDSS-IV) has been in operation since July 2014. This paper describes the second data release from this phase, and the fourteenth from SDSS overall (making this, Data Release Fourteen or DR14). This release makes public data taken by SDSS-IV in its first two years of operation (July 2014-2016). Like all previous SDSS releases, DR14 is cumulative, including the most recent reductions and calibrations of all data taken by SDSS since the first phase began operations in 2000. New in DR14 is the first public release of data from the extended Baryon Oscillation Spectroscopic Survey (eBOSS); the first data from the second phase of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE-2), including stellar parameter estimates from an innovative data driven machine learning algorithm known as "The Cannon"; and almost twice as many data cubes from the Mapping Nearby Galaxies at APO (MaNGA) survey as were in the previous release (N = 2812 in total). This paper describes the location and format of the publicly available data from SDSS-IV surveys. We provide references to the important technical papers describing how these data have been taken (both targeting and observation details) and processed for scientific use. The SDSS website (www.sdss.org) has been updated for this release, and provides links to data downloads, as well as tutorials and examples of data use. SDSS-IV is planning to continue to collect astronomical data until 2020, and will be followed by SDSS-V.Comment: SDSS-IV collaboration alphabetical author data release paper. DR14 happened on 31st July 2017. 19 pages, 5 figures. Accepted by ApJS on 28th Nov 2017 (this is the "post-print" and "post-proofs" version; minor corrections only from v1, and most of errors found in proofs corrected