Improving Defect Prediction Models by Combining Classifiers Predicting Different Defects

Background: The software industry spends a lot of money on finding and fixing defects. It utilises software defect prediction models to identify code that is likely to be defective. Prediction models have, however, reached a performance bottleneck. Any improvements to prediction models would likely yield less defects-reducing costs for companies. Aim: In this dissertation I demonstrate that different families of classifiers find distinct subsets of defects. I show how this finding can be utilised to design ensemble models which outperform other state-of-the-art software defect prediction models. Method: This dissertation is supported by published work. In the first paper I explore the quality of data which is a prerequisite for building reliable software defect prediction models. The second and third papers explore the ability of different software defect prediction models to find distinct subsets of defects. The fourth paper explores how software defect prediction models can be improved by combining a collection of classifiers that predict different defective components into ensembles. An additional, non-published work, presents a visual technique for the analysis of predictions made by individual classifiers and discusses some possible constraints for classifiers used in software defect prediction. Result: Software defect prediction models created by classifiers of different families predict distinct subsets of defects. Ensembles composed of classifiers belonging to different families outperform other ensemble and standalone models. Only a few highly diverse and accurate base models are needed to compose an effective ensemble. This ensemble can consistently predict a greater number of defects compared to the increase in incorrect predictions. Conclusion: Ensembles should not use the majority-voting techniques to combine decisions of classifiers in software defect prediction as this will miss correct predictions of classifiers which uniquely identify defects. Some classifiers could be less successful for software defect prediction due to complex decision boundaries of defect data. Stacking based ensembles can outperform other ensemble and stand-alone techniques. I propose new possible avenues of research that could further improve the modelling of ensembles in software defect prediction. Data quality should be explicitly considered prior to experiments for researchers to establish reliable results

A Longitudinal Study of Anti Micro Patterns in 113 Versions of Tomcat

Background: Micro patterns represent design decisions in code. They are similar to design patterns and can be detected automatically. These micro structures can be helpful in identifying portions of code which should be improved (anti-micro patterns), or other well-designed parts which need to be preserved. The concepts expressed in these design decisions are defined at class-level; therefore the primary goal is to detect and provide information related to a specific granularity level. Aim: this paper aims to present preliminary results about a longitudinal study performed on anti-micro pattern distributions over 113 versions of Tomcat. Method: we first extracted the micro patterns from the 113 versions of Tomcat, then found the percentage of classes matching each of the six anti-micro pattern considered for this analysis, and studied correlations among the obtained time series after testing for stationarity, randomness and seasonality. Results: results show that the time series are stationary, not random (except for Function Pointer), and that additional studied are needed for studying seasonality. Regarding correlations, only the Pool and Record time series presented a correlation of 0.69, while moderate correlation has been found between Function Pointer and Function Object (0.58) and between Cobol Like and Pool (0.44)

Getting defect prediction into industrial practice:The ELFF tool

Defect prediction has been the subject of a great deal of research over the last two decades. Despite this research it is increasingly clear that defect prediction has not transferred into industrial practice. One of the reasons defect prediction remains a largely academic activity is that there are no defect prediction tools that developers can use during their day-to-day development activities. In this paper we describe the defect prediction tool that we have developed for industrial use. Our ELFF tool seamlessly plugs into the IntelliJ IDE and enables developers to perform regular defect prediction on their Java code. We explain the state-of-art defect prediction that is encapsulated within the ELFF tool and describe our evaluation of ELFF in a large UK telecommunications company

The Maunakea Spectroscopic Explorer Book 2018

(Abridged) This is the Maunakea Spectroscopic Explorer 2018 book. It is intended as a concise reference guide to all aspects of the scientific and technical design of MSE, for the international astronomy and engineering communities, and related agencies. The current version is a status report of MSE's science goals and their practical implementation, following the System Conceptual Design Review, held in January 2018. MSE is a planned 10-m class, wide-field, optical and near-infrared facility, designed to enable transformative science, while filling a critical missing gap in the emerging international network of large-scale astronomical facilities. MSE is completely dedicated to multi-object spectroscopy of samples of between thousands and millions of astrophysical objects. It will lead the world in this arena, due to its unique design capabilities: it will boast a large (11.25 m) aperture and wide (1.52 sq. degree) field of view; it will have the capabilities to observe at a wide range of spectral resolutions, from R2500 to R40,000, with massive multiplexing (4332 spectra per exposure, with all spectral resolutions available at all times), and an on-target observing efficiency of more than 80%. MSE will unveil the composition and dynamics of the faint Universe and is designed to excel at precision studies of faint astrophysical phenomena. It will also provide critical follow-up for multi-wavelength imaging surveys, such as those of the Large Synoptic Survey Telescope, Gaia, Euclid, the Wide Field Infrared Survey Telescope, the Square Kilometre Array, and the Next Generation Very Large Array.Comment: 5 chapters, 160 pages, 107 figure

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Using different characteristics of machine learners to identify different defect families

Background: Software defect prediction has been an active area of research for the last few decades. Many models have been developed with aim to find locations in code likely to contain defects. As of yet, these prediction models are of limited use and rarely used in the software industry. Problem: Current modelling techniques are too coarse grained and fail in finding some defects. Most of the prediction models do not look for targeted defect characteristics, but rather treat them as a black box and homogeneous. No study has investigated in greater detail how well certain defect characteristics work with different prediction modelling techniques. Methodology: This PhD will address three major tasks. First, the relation among software defects, prediction models and static code metrics will be analysed. Second, the possibility of a mapping function between prediction models and defect characteristics shall be investigated. Third, an optimised ensemble model that searches for targeted defects will be developed. Contribution: A few contributions will yield from this work. Characteristics of defects will be identified, allowing other researchers to build on this work to produce more efficient prediction models in future. New modelling techniques that better suit state-of-the-art knowledge in defect prediction shall be designed. Such prediction models should be transformed in a tool that can be used by our industrial collaborator in the real industry environment

Software structure evolution and relation to system defectiveness

We still do not have clear figure about how software systems evolve and how we may control its evolution process. Software structure has been identified that may have the biggest impact, especially because it may be represented from numerous perspectives. Novelty introduced in this paper is the way how we define the structure of evolving complex software systems. The structure is represented with help of graph representations, and subgraph frequencies, the concept reused from the network analysis theory. The graph structure of a software system and its evolution over the system versions, as well as its relation to defectiveness, is empirically studied in terms of subgraph frequencies and motifs for more than 30 releases of three large open source software systems. We identified that the same set of subgraphs of software system is present across the system version, but different sets, although overlapping, are present in different software systems. Furthermore, we confirmed the continuous system evolution in terms of continuous structure change and we find some evidence for its relation to system defectiveness

LA REDÉFINITION DU POUVOIR DANS UNE SOCIÉTÉ POST-SOVIÉTIQUE (L'OUZBEKISTAN )

PARIS-Musée de l'Homme (751162302) / SudocPARIS-Médiathèque MQB (751132304) / SudocPARIS-Fondation MSH (751062301) / SudocSudocFranceF

Commémorer

Commémorer et célébrer le passé au présent est devenu un enjeu contemporain. Il peut s'agir du maintien et de la remémoration de traces, d'édifices, de personnages mythiques ou de pratiques incarnées par des survivants. Des chercheurs, sociologues et anthropologues, participent à dégager de l'oubli et de la poussière et à réactualiser des pans réels ou imaginés de l'Histoire... Telles sont les perspectives proposées ici