Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Quantitative Methods in Object-Oriented Software Engineering

This paper includes a brief description of the author’s doctoral research work in Quantitative Methods applied to the Object-Oriented Software Engineering field. Previous, current and future research work are outlined. An overview of related work is also included

Comparison of method chunks and method fragments for situational method engineering

Two main candidates for the atomic element to be used in Situational Method Engineering (SME) have been proposed: the “method fragment ” and the “method chunk”. These are examined here in terms of their conceptual integrity and in terms of how they may be used in method construction. Also, parallels are drawn between the two approaches. Secondly, the idea of differentiating an interface from a body has been proposed for method chunks (but not for method fragments). This idea is examined and mappings are constructed between the interface and body concepts of method chunks and the concepts used to describe method fragments. The new ISO/IEC 24744 standard metamodel is used as a conceptual framework to perform these mappings

An overview of models and standards of processes in the SE, SwE and IS disciplines

This chapter develops a descriptive-conceptual overview of the main models and standards of processes formulated in the systems engineering (SE), software engineering (SwE) and information systems (IS) disciplines. Given the myriad of models and standards reported, the convergence suggested for the SE and SwE models and standards and the increasing complexity of the modern information systems, we argue that these ones become relevant in the information systems discipline. Firstly, we report the ratio- nale for having models and standards of processes in SE, SwE and IS. Secondly, we review their main Overview of Models and Standards of Processes in the SE, SwE, and IS Disciplines characteristics. Thirdly, based on the identified aims and principles, we report and posit the concepts of process, system and service as conceptual building blocks for describing such models and standards. Finally, initial theoretical and practical implications for the information systems discipline of such models and standards are discussed, as well as recommendations for further research are suggested

Computer Vision for Marine Environmental Monitoring

Osterloff J. Computer Vision for Marine Environmental Monitoring. Bielefeld: Universität Bielefeld; 2018.Ocean exploration using imaging techniques has recently become very popular as camera systems became affordable and technique developed further. Marine imaging provides a unique opportunity to monitor the marine environment. The visual exploration using images allows to explore the variety of fauna, flora and geological structures of the marine environment. This monitoring creates a bottleneck as a manual evaluation of the large amounts of underwater image data is very time consuming. Information encapsulated in the images need to be extracted so that they can be included in statistical analyzes. Objects of interest (OOI) have to be localized and identified in the recorded images. In order to overcome the bottleneck, computer vision (CV) is applied in this thesis to extract the image information (semi-) automatically. A pre-evaluation of the images by marking OOIs manually, i.e. the manual annotation process, is necessary to provide examples for the applied CV methods. Five major challenges are identified in this thesis to apply of CV for marine environmental monitoring. The challenges can be grouped into challenges caused by underwater image acquisition and by the use of manual annotations for machine learning (ML). The image acquisition challenges are the optical properties challenge, e.g. a wavelength dependent attenuation underwater, and the dynamics of these properties, as different amount of matter in the water column affect colors and illumination in the images. The manual annotation challenges for applying ML for underwater images are, the low number of available manual annotations, the quality of the annotations in terms of correctness and reproducibility and the spatial uncertainty of them. The latter is caused by allowing a spatial uncertainty to speed up the manual annotation process e.g. using point annotations instead of fully outlining OOIs on a pixel level. The challenges are resolved individually in four different new CV approaches. The individual CV approaches allow to extract new biologically relevant information from time-series images recorded underwater. Manual annotations provide the ground truth for the CV systems and therefore for the included ML. Placing annotations manually in underwater images is a challenging task. In order to assess the quality in terms of correctness and reproducibility a detailed quality assessment for manual annotations is presented. This includes the computation of a gold standard to increase the quality of the ground truth for the ML. In the individually tailored CV systems, different ML algorithms are applied and adapted for marine environmental monitoring purposes. Applied ML algorithms cover a broad variety from unsupervised to supervised methods, including deep learning algorithms. Depending on the biologically motivated research question, systems are evaluated individually. The first two CV systems are developed for the _in-situ_ monitoring of the sessile species _Lophelia pertusa_. Visual information of the cold-water coral is extracted automatically from time-series images recorded by a fixed underwater observatory (FUO) located at 260 m depth and 22 km off the Norwegian coast. Color change of a cold water coral reef over time is quantified and the polyp activity of the imaged coral is estimated (semi-) automatically. The systems allow for the first time to document an _in-situ_ change of color of a _Lophelia pertusa_ coral reef and to estimate the polyp activity for half a year with a temporal resolution of one hour. The third CV system presented in this thesis allows to monitor the mobile species shrimp _in-situ_. Shrimp are semitransparent creating additional challenges for localization and identification in images using CV. Shrimp are localized and identified in time-series images recorded by the same FUO. Spatial distribution and temporal occurrence changes are observed by comparing two different time periods. The last CV system presented in this thesis is developed to quantify the impact of sedimentation on calcareous algae samples in a _wet-lab_ experiment. The size and color change of the imaged samples over time can be quantified using a consumer camera and a color reference plate placed in the field of view for each recorded image. Extracting biologically relevant information from underwater images is only the first step for marine environmental monitoring. The extracted image information, like behavior or color change, needs to be related to other environmental parameters. Therefore, also data science methods are applied in this thesis to unveil some of the relations between individual species' information extracted semi-automatically from underwater images and other environmental parameters

A Theory of Class

We present a mathematical theory of class. The theory is general, in that it encompasses many different approaches to type abstraction, such as type constructors, generic parameters, classes, inheritance and polymorphism. The theory is elegant, in that it is based on a simple Generalization of F-bounds. The theory has timely implications for emerging OMG standards and future language designs

Educational Considerations, vol. 24 (1) Full Issue

Educational Considerations, vol. 24 (1) Fall 1996 - Full issu