Intelligent software quality and assessment model based on software behavioral and human factor approach

Software quality is a research area that falls in the nich of cyberspace security in the National Strategic Information and Communication Technology (ICT) Roadmap.This roadmap indicates that the cyberspace encompases not only the use of Internet but also less visible systems and infrastructures.The growing dependence of these systems brings about the critical need to protect sotware from destruction and incapacitation.Previous research has identified factors and attributes for static quality model.This research report discussess the construction of a dynamic software quality model for effective software assessment that assists in the development of quality software in the organisations. The proposed model consists of two main software quality attributes: the behavioral and the human aspect.These two components of quality produce a balance model between technical requirement and human factor.The available software quality model does not accommodate all the aspects of software quality requirements.The proposed dynamic model is capable to identify and recommend to the environment if there are any new attributes to be included in the model.This c an be done using filter wrapper based feature ranking technique.The objectives of this research are; to investigate the quality and assessment problems in software product; to develop a theoritical framework for intelligent software quality model ; to propose feature ranking technique for software quality and assessment; and to construct an intelligent software quality and assessment model. The research is conducted in five main phases which include: 1) the theoretical study; 2) design of formal framework on intelligent software quality and assessment; 3) model the software quality using Artificial Intelligence technique; 4) construction of intelligent model of software quality and assessment and; 5) conformation study.With respect to other ranking technique for software quality attribute, the proposed technique depicted a higher correlation with human experts.The model provides a way to assess software product using intelligent technique, which is useful for organization in selection and assessment of software as well as to integrate in future researches as a quality benchmark. This also ensure that the quality of the software meets the nation’s and organisation’s requirements and standards in current and future time

An Examination Of Faculty Innovativeness In Relation To Inductive Teaching And The Use Of Technology

The conceptual framework for this research describes the processes faculty use to create an online course that meets learning outcomes while creating a positive learning experience for the online student.  This involves acceptance of technology to create a course structured for inductive learning in addition to traditional deductive learning.  The presence of faculty innovation is demonstrated by the design of inductive learning exercises.  Innovative professors must continue to enhance the quality of their online delivery in an effort to achieve the same educational outcomes acquired in a traditional classroom.  However, many professors are reluctant to embrace online delivery software and technology designed to enhance the classroom.  This research identifies innovation, technology acceptance, inductive learning, and quality enhancement as measures online quality in an academic environment.  The assessment instrument for this research is the Rubric for Online Competencies and Standards (ROCS) developed by Hodges University.  The ROCS consist of twenty-eight quality indicators in six categories extracted from a combination of regional standards from accreditation bodies including, but not limited to: SACS, Sloan-C, and EDUCAUSE

Threat Assessment and Carbon Estimation in Bolgoda Lake Environs

Threat assessment is a scientific process that is especially useful in conservation and management prioritisation. The nature conservancy’s eco regional planning process to develop a conservation framework one such method that identifies “threat” as a key component. The study aims to use the above method to evaluate, Bolgoda an ecologically important wetland in Sri Lanka Bolgoda which spreads in 374 km2. Sampling locations were systematically selected to assess direct and indirect threats. Panadura, Moratuwa, Bandaragama and Thalpitiya were the sampling locations. Panadura, Moratuwa, Kesbewa and Bandaragama DSDs were selected to Carbon Model. InVEST Carbon model was employed with a view to valuing carbon storage and mapping carbon storage as benefits with temporal characters of land cover changing in two points in time (Year 2000 to 2018). Data for the threat assessment was mainly collected by conducting a questionnaire (68) and field (8) surveys. Inputs for carbon model were prepared using USGS satellite data sets with ArcGIS™ 10.4 remote sensing software. Results disclosures that Moratuwa and Panadura semi urban areas are having intense threats. However, Invasive alien species, water and ground pollution, reducing of vegetation and loss of continuous habitats are the existing direct threats while indirect threats function as sources for direct threats i.e. Illegal land filling, solid waste dumping, unauthorised constructions, discharging industrial effluent and forest clearing. The questionnaire survey was analysed using SPSS™ 21 package. Satisfaction of community on conservation of Bolgoda is low (26.1%). With the traditionalism and their education level, their behavior with the environment is varying. In this study, vegetation cover was considered as an indicator of the habitat quality of Lake Environment. Land use change detection disclosures the declining of vegetation cover with the time (45.19%). InVEST models realised that forest cover has been declined resulted in diminishing carbon storage (26.25 mg C per pixel) and the habitat quality. Hence, this study provides scientific backing for the conservation on the lake system.Keywords: InVEST, Threat, Carbon storage, Habitat quality, Bolgoda, Nature conservanc

Assessing and improving the quality of model transformations

Software is pervading our society more and more and is becoming increasingly complex. At the same time, software quality demands remain at the same, high level. Model-driven engineering (MDE) is a software engineering paradigm that aims at dealing with this increasing software complexity and improving productivity and quality. Models play a pivotal role in MDE. The purpose of using models is to raise the level of abstraction at which software is developed to a level where concepts of the domain in which the software has to be applied, i.e., the target domain, can be expressed e??ectively. For that purpose, domain-speci??c languages (DSLs) are employed. A DSL is a language with a narrow focus, i.e., it is aimed at providing abstractions speci??c to the target domain. This makes that the application of models developed using DSLs is typically restricted to describing concepts existing in that target domain. Reuse of models such that they can be applied for di??erent purposes, e.g., analysis and code generation, is one of the challenges that should be solved by applying MDE. Therefore, model transformations are typically applied to transform domain-speci??c models to other (equivalent) models suitable for di??erent purposes. A model transformation is a mapping from a set of source models to a set of target models de??ned as a set of transformation rules. MDE is gradually being adopted by industry. Since MDE is becoming more and more important, model transformations are becoming more prominent as well. Model transformations are in many ways similar to traditional software artifacts. Therefore, they need to adhere to similar quality standards as well. The central research question discoursed in this thesis is therefore as follows. How can the quality of model transformations be assessed and improved, in particular with respect to development and maintenance? Recall that model transformations facilitate reuse of models in a software development process. We have developed a model transformation that enables reuse of analysis models for code generation. The semantic domains of the source and target language of this model transformation are so far apart that straightforward transformation is impossible, i.e., a semantic gap has to be bridged. To deal with model transformations that have to bridge a semantic gap, the semantics of the source and target language as well as possible additional requirements should be well understood. When bridging a semantic gap is not straightforward, we recommend to address a simpli??ed version of the source metamodel ??rst. Finally, the requirements on the transformation may, if possible, be relaxed to enable automated model transformation. Model transformations that need to transform between models in di??erent semantic domains are expected to be more complex than those that merely transform syntax. The complexity of a model transformation has consequences for its quality. Quality, in general, is a subjective concept. Therefore, quality can be de??ned in di??erent ways. We de??ned it in the context of model transformation. A model transformation can either be considered as a transformation de??nition or as the process of transforming a source model to a target model. Accordingly, model transformation quality can be de??ned in two di??erent ways. The quality of the de??nition is referred to as its internal quality. The quality of the process of transforming a source model to a target model is referred to as its external quality. There are also two ways to assess the quality of a model transformation (both internal and external). It can be assessed directly, i.e., by performing measurements on the transformation de??nition, or indirectly, i.e., by performing measurements in the environment of the model transformation. We mainly focused on direct assessment of internal quality. However, we also addressed external quality and indirect assessment. Given this de??nition of quality in the context of model transformations, techniques can be developed to assess it. Software metrics have been proposed for measuring various kinds of software artifacts. However, hardly any research has been performed on applying metrics for assessing the quality of model transformations. For four model transformation formalisms with di??fferent characteristics, viz., for ASF+SDF, ATL, Xtend, and QVTO, we de??ned sets of metrics for measuring model transformations developed with these formalisms. While these metric sets can be used to indicate bad smells in the code of model transformations, they cannot be used for assessing quality yet. A relation has to be established between the metric sets and attributes of model transformation quality. For two of the aforementioned metric sets, viz., the ones for ASF+SDF and for ATL, we conducted an empirical study aiming at establishing such a relation. From these empirical studies we learned what metrics serve as predictors for di??erent quality attributes of model transformations. Metrics can be used to quickly acquire insights into the characteristics of a model transformation. These insights enable increasing the overall quality of model transformations and thereby also their maintainability. To support maintenance, and also development in a traditional software engineering process, visualization techniques are often employed. For model transformations this appears as a feasible approach as well. Currently, however, there are few visualization techniques available tailored towards analyzing model transformations. One of the most time-consuming processes during software maintenance is acquiring understanding of the software. We expect that this holds for model transformations as well. Therefore, we presented two complementary visualization techniques for facilitating model transformation comprehension. The ??rst-technique is aimed at visualizing the dependencies between the components of a model transformation. The second technique is aimed at analyzing the coverage of the source and target metamodels by a model transformation. The development of the metric sets, and in particular the empirical studies, have led to insights considering the development of model transformations. Also, the proposed visualization techniques are aimed at facilitating the development of model transformations. We applied the insights acquired from the development of the metric sets as well as the visualization techniques in the development of a chain of model transformations that bridges a number of semantic gaps. We chose to solve this transformational problem not with one model transformation, but with a number of smaller model transformations. This should lead to smaller transformations, which are more understandable. The language on which the model transformations are de??ned, was subject to evolution. In particular the coverage visualization proved to be bene??cial for the co-evolution of the model transformations. Summarizing, we de??ned quality in the context of model transformations and addressed the necessity for a methodology to assess it. Therefore, we de??ned metric sets and performed empirical studies to validate whether they serve as predictors for model transformation quality. We also proposed a number of visualizations to increase model transformation comprehension. The acquired insights from developing the metric sets and the empirical studies, as well as the visualization tools, proved to be bene??cial for developing model transformations

Considerations about quality in model-driven engineering

The final publication is available at Springer via http://dx.doi.org/10.1007/s11219-016-9350-6The virtue of quality is not itself a subject; it depends on a subject. In the software engineering field, quality means good software products that meet customer expectations, constraints, and requirements. Despite the numerous approaches, methods, descriptive models, and tools, that have been developed, a level of consensus has been reached by software practitioners. However, in the model-driven engineering (MDE) field, which has emerged from software engineering paradigms, quality continues to be a great challenge since the subject is not fully defined. The use of models alone is not enough to manage all of the quality issues at the modeling language level. In this work, we present the current state and some relevant considerations regarding quality in MDE, by identifying current categories in quality conception and by highlighting quality issues in real applications of the model-driven initiatives. We identified 16 categories in the definition of quality in MDE. From this identification, by applying an adaptive sampling approach, we discovered the five most influential authors for the works that propose definitions of quality. These include (in order): the OMG standards (e.g., MDA, UML, MOF, OCL, SysML), the ISO standards for software quality models (e.g., 9126 and 25,000), Krogstie, Lindland, and Moody. We also discovered families of works about quality, i.e., works that belong to the same author or topic. Seventy-three works were found with evidence of the mismatch between the academic/research field of quality evaluation of modeling languages and actual MDE practice in industry. We demonstrate that this field does not currently solve quality issues reported in industrial scenarios. The evidence of the mismatch was grouped in eight categories, four for academic/research evidence and four for industrial reports. These categories were detected based on the scope proposed in each one of the academic/research works and from the questions and issues raised by real practitioners. We then proposed a scenario to illustrate quality issues in a real information system project in which multiple modeling languages were used. For the evaluation of the quality of this MDE scenario, we chose one of the most cited and influential quality frameworks; it was detected from the information obtained in the identification of the categories about quality definition for MDE. We demonstrated that the selected framework falls short in addressing the quality issues. Finally, based on the findings, we derive eight challenges for quality evaluation in MDE projects that current quality initiatives do not address sufficiently.F.G, would like to thank COLCIENCIAS (Colombia) for funding this work through the Colciencias Grant call 512-2010. This work has been supported by the Gene-ralitat Valenciana Project IDEO (PROMETEOII/2014/039), the European Commission FP7 Project CaaS (611351), and ERDF structural funds.Giraldo-Velásquez, FD.; España Cubillo, S.; Pastor López, O.; Giraldo, WJ. (2016). Considerations about quality in model-driven engineering. Software Quality Journal. 1-66. https://doi.org/10.1007/s11219-016-9350-6S166(1985). Iso information processing—documentation symbols and conventions for data, program and system flowcharts, program network charts and system resources charts. ISO 5807:1985(E) (pp. 1–25).(2011). Iso/iec/ieee systems and software engineering – architecture description. 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Generation and Provision of Ship's Master Track Data and Metadata for Standardized Access

This thesis deals with the navigation tracks of the Research Vessel Polarstern and it provides a software application for statistical analysis of these tracks. The scope of this program follows two requirements. Firstly the program provides an assessment functionality for post processing of old tracks of R/V Polarstern to include all these tracks in long term storage and archiving system of AWI, the PANGAEA network. Secondly, it provides an online assessment tool to analyze the navigation information onboard the vessel in real time. The software application has been developed using Borland C++ Builder 6 environment under Windows 7 operating system. The numeric functions of this application are following ANSI standard of C++ programming language, and thus it could be implemented in any developing environments using this standard. A full description of the navigation systems onboard the R/V Polarstern has been reviewed to investigate how the possible connections and relations between different sensors act, to understand how the cruise track is produced and to find appropriate analysis methods. In particular, a statistical method is introduced for analyzing the navigational data of the vessel from different sources onboard. This method consists of several tests for detecting and identifying the outliers in the data. It follows a decision based filter that keeps the original data if it successfully passes the outlier tests. In addition, the filter replaces the outliers with appropriate solutions that are calculated using different routines such as transformation from different devices as well as interpolation and extrapolation procedures. This method provokes smoothing of the original data as well. As results of the online and the offline mode, the final product is a file, called “Master track”, which consists of seven columns. These columns are: the date and time of the records with one second interval, the evaluated position of the vessel given in geographical coordinates, the heading of the ship, the roll and pitch and finally a quality number that indicates the precision of the position. In addition a generalized version of the Master track is provided applying Ramer–Douglas–Peucker algorithm. At the end of this work some results are presented to show the improvements that have been achieved using the application. A sample Master track and the corresponding generalized track were finally published in PANGAEA

Core curricular elements of effective undergraduate technology management academic programs

Technology management skills have become increasingly important to employers in today’s rapidly changing technological environment; yet a scarcity of research exists regarding desired core competencies of undergraduate technology management majors. The purpose of this study was to determine the core curricular elements of an effective undergraduate technology management academic program. A quantitative mixed-mode (Internet-based and paper-based) survey design using a 5-point Likert rating (strongly agree, somewhat agree, neither agree nor disagree, somewhat disagree, and strongly disagree) was used to solicit opinions from members of the sample population regarding core curricular elements of effective undergraduate technology management programs. Implementation of this research project included the following 5 phases: (a) identification of the sample population, (b) selection of survey software, (c) survey instrument design and pilot testing, (d) data gathering, and (e) data analysis. This exploratory descriptive study employed purposive expert sampling of 180 people with technology management expertise in four industry sectors (i.e., business services, education, government, and manufacturing); in addition, 18 executive board members of the Michigan Economic Development Corporation and 30 members of the Southern Wayne County Chamber of Commerce were queried. Information regarding the relative perceived importance of each of the following eight core technology management competency areas was sought: (a) strategic management of technology, (b) management of innovation and product development, (c) management of technological change, (d) management of organizational change, (e) project management, (f) assessment and evaluation of technology, (g) quality management of technology, and (h) information and knowledge management. Significance was determined at the .05 level

Integrating automated support for a software management cycle into the TAME system

Software managers are interested in the quantitative management of software quality, cost and progress. An integrated software management methodology, which can be applied throughout the software life cycle for any number purposes, is required. The TAME (Tailoring A Measurement Environment) methodology is based on the improvement paradigm and the goal/question/metric (GQM) paradigm. This methodology helps generate a software engineering process and measurement environment based on the project characteristics. The SQMAR (software quality measurement and assurance technology) is a software quality metric system and methodology applied to the development processes. It is based on the feed forward control principle. Quality target setting is carried out before the plan-do-check-action activities are performed. These methodologies are integrated to realize goal oriented measurement, process control and visual management. A metric setting procedure based on the GQM paradigm, a management system called the software management cycle (SMC), and its application to a case study based on NASA/SEL data are discussed. The expected effects of SMC are quality improvement, managerial cost reduction, accumulation and reuse of experience, and a highly visual management reporting system

Process capability assessments in small development firms

[Abstract}: Assessment-based Software Process Improvement (SPI) programs such as the Capability Maturity Model (CMM), Bootstrap, and SPICE (ISO/IEC 15504) are based on formal frameworks and promote the use of systematic processes and management practices for software development. These approaches identify best practices for the management of software development and when applied, enable organizations to understand, control and improve development processes. The purpose of a SPI assessment is to compare the current processes used in an organization with a list of recommended or ‘best’ practices. This research investigates the adoption of SPI initiatives by four small software development firms. These four firms participated in a process improvement program which was sponsored by Software Engineering Australia (SEA) (Queensland). The assessment method was based on SPICE (ISO/IEC 15504) and included an initial assessment, recommendations, and a follow-up meeting. For each firm, before and after snapshots are provided of the capability as assessed on eight processes. The discussion which follows summarizes the improvements realized and considers the critical success factors relating to SPI adoption for small firms