1,357 research outputs found
Interview with Associate Professor L. Lenore Layman
No full textOral history interview with Associate Professor L. Lenore Layman, former staff member at Murdoch University.
This sound recording is part of the History of Murdoch University Collection
Automated classification of NASA anomalies using natural language processing techniques
No full textNASA anomaly databases are rich resources of software failure data in the field. These data are often captured in natural language that is not appropriate for trending or statistical analyses. This fast abstract describes a feasibility study of applying 60 natural language processing techniques for automatically classifying anomaly data to enable trend analyses. © 2013 IEEE
Peggy Holmes-Layman
No full textPeggy Holmes-Layman receives an award for 15 years of service in Academic Affairs. (l-r) President William Perry, Peggy Holmes-Layman, Provost Blair Lord.https://thekeep.eiu.edu/years_of_service_2013/1058/thumbnail.jp
Ask the engineers: Exploring repertory grids and personal constructs for software data analysis
No full textMaturity in software projects is often equated with data-driven predictability. However, data collection is expensive and measuring all variables that may correlate with project outcome is neither practical nor feasible. In contrast, a project engineer can identify a handful of factors that he or she believes influence the success of a project. The challenge is to quantify engineers' insights in a way that is useful for data analysis. In this exploratory study, we investigate the repertory grid technique for this purpose. The repertory grid technique is an interview-based procedure for eliciting 'constructs' (e.g., Adhering to coding standards) that individuals believe influence a worldly phenomenon (e.g., What makes a high-quality software project) by comparing example elements from their past (e.g., Projects they have worked on). We investigate the relationship between objective metrics of project performance and repertory grid constructs elicited from eight software engineers. Our results show correlations between the engineers' subjective constructs and the objective project outcome measures. This suggests that repertory grids may be of benefit in developing models of project outcomes, particularly when project data is limited
Bayesian Layman metrics.
No full text<p>Density plot showing the uncertainty of the Bayesian Layman metrics (NR = δ<sup>15</sup>N range, CR = δ<sup>13</sup>C range, CD = mean distance to centroid, MNND = mean nearest neighbour distance, SDNND = standard deviation of the nearest neighbour distance) for different combinations of location (BMSM <i>vs</i>. Boulogne), sampling area (<i>L</i>. <i>conchilega</i> aggregation <i>vs</i>. control) and period (spring <i>vs</i>. autumn). Black dots represent the modes, while the shaded boxes represent the 50% (dark grey), 75% (light grey) and 95% (white) credible intervals. Note the different scales of distance (‰) for NR and CR <i>vs</i>. CD, MNND and SDNND. SL = spring-<i>L</i>. <i>conchilega</i> aggregation; SC = spring-control; AL = autumn-<i>L</i>. <i>conchilega</i> aggregation; AC = autumn-control.</p
Technical debt: Showing the way for better transfer of empirical results
No full textIn this chapter, we discuss recent progress and opportunities in empirical software engineering by focusing on a particular technology, Technical Debt (TD), which ties together many recent developments in the field. Recent advances in TD research are providing empiricists the chance to make more sophisticated recommendations that have observable impact on practice. TD uses a financial metaphor and provides a framework for articulating the notion of tradeoffs between the short-term benefits and the long-term costs of software development decisions. TD is seeing an explosion of interest in the practitioner community, and research in this area is quickly having an impact on practice. We argue that this is due to several strands of empirical research reaching a level of maturity that provides useful benefits to practitioners, who in turn provide excellent data to researchers. They key is providing observable benefit to practitioners, such as the ability to tie technical debt measures to business goals, and the ability to articulate more sophisticated value-based propositions regarding how to prioritize rework. TD is an interesting case study in how the maturing field of empirical software engineering research is paying dividends. It is only a little hyperbolic to call this a watershed moment for empirical study, where many areas of progress are coming to a head at the same time
Characterization of cull sows harvested in the U.S.
No full textStalder, K.J.; Knauer, M.; Karriker, L.; Baas, T.J.; Johnson, C.; Serenius, T.; Layman, L.; Mabry, J. W.; McKean, J.D.. (2006). Characterization of cull sows harvested in the U.S.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/157430
Performance and economic evaluation of feeding cull sows
No full textFitzgerald, R.; Stalder, K.J.; Karriker, L.; Knauer, M.; Johnson, C.; Baas, T.J.; Layman, L.; Mabry, J. W.. (2006). Performance and economic evaluation of feeding cull sows. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/157432
Applying Software Data Analysis in Industry Contexts: When Research Meets Reality
No full textSoftware data analytics is key for helping stakeholders make decisions, and thus establishing a measurement and data analysis program is a recognized best practice within the software industry. However, practical implementation of measurement programs and analytics in industry is challenging. In this chapter, we discuss real-world challenges that arise during the implementation of a software measurement and analytics program. We also report lessons learned for overcoming these challenges and best practices for practical, effective data analysis in industry. The lessons learned provide guidance for researchers who wish to collaborate with industry partners in data analytics, as well as for industry practitioners interested in setting up and realizing the benefits of an effective measurement program
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