Quantitative Approaches in Object-Oriented Software Engineering

Object-oriented software metrics are important in software engineering field for measurement of the software quality, size estimation, complexity and effort. Object oriented software development is the most popular and best practice in software development. Object oriented software metrics can be used for measuring extend of object orientedness of a program. These quantities are important information indicators, that can be helpful for a software developer in take decisions for designing good software. It is proven that the software metrics provide quantitative information about the structure of the code, which can be used to determine the reliability of software. The design of the software is highly recommended to be object oriented in the software development environment. Software metrics specific to programs that have object oriented design can be used to measure the quality of the software like inheritance, assessing the effects of coupling, the complexity of the code, size aspects, etc, of the software developed in such an environment. Software measurement is a way for assessing the quality of software. Many attributes can define quality of a software, internal (size, complexity) as well as external (reliability)[14]. Often internal attributes are contributing factors to external attributes. Reliability as a term means the degree to which a component, product or system executes specified functions for a specific period of time under specified conditions. Software reliability is the software being trustworthy of the specifications. The aim of the work is to model the relationship between reliability of object oriented software to the object oriented design metrics and predict reliability of software.

In the context of the rapid development of information technologies, software quality is becoming critical for the successful operation of organizations in various industries. The growing complexity of modern software solutions requires the involvement of highly qualified specialists in software testing and quality assessment, capable of effectively identifying shortcomings and ensuring that the product meets established standards. At the same time, assessing the level of competence of such experts remains a difficult task, which is often based on subjective criteria and methods. The relevance of the study is due to the acute need of the modern IT market for objective tools for assessing the professional level of specialists, especially in the field of software quality assurance. Traditional approaches to qualification assessment, such as interviews, test tasks or resume analysis, often do not provide a complete and objective picture of the expert's competence. This problem becomes especially acute in the conditions of the global labor market, when companies are forced to evaluate specialists remotely, relying only on a limited set of data on their experience and skills. Today, software has become an integral part of many areas of our everyday life - from automation and optimization of production processes to creating comfort for an individual. The object of the study is the process of determining the level of competence of experts in software quality assessment. The subject of the study is a mathematical model for calculating the level of competence of an expert. The practical value of the results of the work is determined by the possibility of using the developed system by HR managers for effective selection of specialists, by heads of QA departments for the formation of balanced testing teams, by certification centers for objective assessment of competence, as well as by the experts themselves for planning their own professional development. Conclusion the developed mathematical model for calculating the level of competence of an expert allows you to reduce the time for assessing the competence of specialists, minimize the influence of subjective factors when making personnel decisions, and optimize the distribution of human resources in software development and testing projects.

In various fields of computer science, rapidly growing hardware power, such as high-speed network, high-performance CPU, huge disk capacity, and large memory space, has been fruitfully harnessed. Examples of such usage are large scale data and web mining, grid computing, and multimedia environments. We propose that such rich hardware can also catapult software engineering to the next level. Huge amounts of software engineering data can be systematically collected and organized from tens of thousands of projects inside organizations, or from outside an organization through the Internet. The collected data can be analyzed extensively to extract and correlate multi-project knowledge for improving organization-wide productivity and quality. We call such an approach for software engineering Mega Software Engineering. In this paper, we propose the concept of Mega Software Engineering, and demonstrate some novel data analysis characteristic of Mega Software Engineering. We describe a framework for enabling Mega Software Engineering.KeywordsSoftware EngineeringSoftware EngineerSoftware ProjectCollaborative FilterGlobal Software DevelopmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Quantitative Methods in Public Administration: Their Use and Development Through Time

Quality model is regarded as a well-accepted approach for assessing, managing and improving software product quality. There are three categories of quality models for software products, i.e., definition model, assessment model, and prediction model. Quality assessment model (QAM) is a metric-based approach to assess the software quality. It is typically regarded as of high importance for its clear method on how to assess a system. However, the current state-of-the-art in QAM research is under limited investigation. To address this gap, the paper provides an organized and synthesized summary of the current QAMs. In detail, we conduct a systematic mapping study (SMS) for structuring the relevant articles. We obtain a total of 716 papers from the five databases, and 31 papers are selected as relevant studies at last. In summary, our work focuses on QAMs from the following aspects: software metrics, quality factors, aggregation methods, evaluation methods and tool support. According to the analysis results, our work discovers five needs that researchers in this area should continue to address: (1) new method and criteria to tailor a quality framework (i.e., structure of software metrics and quality factors) according to different specifics, (2) systematic investigations on the effectiveness, strength and weakness of different aggregation methods to guide the method selection in different context, (3) more investigations on evaluating QAMs in the context of industrial cases, (4) further investigations or real-world case studies on the QAMs related tools, and (5) building a public and diverse software benchmark which can be adopted in different application context.

In a wide range of application areas, system behavior is modeled using variations of scenarios and state machines. In telecommunications, ITU standards SDL (Specification and Description Language) and MSC (Message Sequence Charts) are used for formal specifications and descriptions. Also, for behavioral modeling of object-oriented software systems, UML interaction diagrams (sequence or collaboration diagrams) and statechart diagrams are commonly used. Use cases can be conveniently refined using interaction diagrams, which in turn aid in recognizing class operations and associations and in building statechart specifications of object behaviour. The aim of this workshop is to build a shared understanding on the relation between scenarios and state machines and to gain insight on techniques and tools that may leverage from combining these approaches to behaviour modeling. In this workshop, we will discuss the usage of scenario-based approaches in software engineering. An emphasis area is the relation between scenarios and state-machines. Areas of interest of this one-day workshop include, but are not limited to the following three main discussion topics: 1) Models and notations (requirements for different application areas, shortcomings in current notations, new suggestions for models or notations, categorizations); 2) Algorithms (e.g., synthesizing state machines from scenarios, implied scenarios, generating scenarios from state machines, consistency checks); 3) Tools (tool support for the issues above, different application areas). Moreover, we will identify the limitations and shortcomings of current approaches and outline possible directions for future research in the domain. A related workshop (scenario-based roundtrip eng.) was organized at OOPSLA 2000.

The selection of software metrics for building software quality prediction models is a search‐based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault‐proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real‐world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd.

Quality of software is the key to successful software development and physics results transparency. An original method to assess the quality of scientific software systems objectively has been developed. A set of established software metrics is combined with statistical analysis methods, such as trend analysis and inequality and diversity measures, to quantify various characteristics of the code, such as maintainability. This methodology, previously applied to Geant4, has now been employed to assess the quality of ROOT, a software system for big data processing, statistical analysis, visualisation and storage frequently used in particle and nuclear physics experiments. Preliminary results of the analysis of ROOT software quality are presented.

A methodology for generating an optimum software quality classification tree using software complexity metrics to discriminate between high-quality modules and low-quality modules is proposed. The process of tree generation is an application of the AIC (Akaike Information Criterion) procedures to the binomial distribution. AIC procedures are based on maximum likelihood estimation and the least number of complexity metrics. It is an improvement of the software quality classification tree generation method proposed by Porter and Selby (1990) from the viewpoint that the complexity metrics are minimized. The problems of their method are that the software quality prediction model is unstable because it reflects observational errors in real data too much and there is no objective criterion for determining whether the discrimination is appropriate or not at a deep nesting level of the classification tree when the number of sample modules gets smaller. To solve these problems a new metric is introduced and its validity is theoretically and experimentally verified. In our examples, complexity metrics written in C language, such as lines of source code, Halstead's (1977) software science, McCabe's (976) cyclomatic number, Henry and Kafura's (1981) fan-in/out and Howatt and Baker's (1989) scope number, are investigated. Our experiments with a medium-sized piece of software (85 thousand lines of source code; 562 samples) show that the software quality classification tree generated by our new metric identifies the target class of the observed modules more efficiently using the minimum number of complexity metrics without any significant decrease of the correct classification ratio (76%->72%) than the conventional classification tree.

The chapter provides possible directions for the wider application of the systems approach to information systems development. Potential improvement of software development practices is linked by some leading experts to the application of more systemic ideas. However, the current state of the practice in software engineering and information systems development shows the urgent need for improvement through greater application of systems thinking.

Software quality is one of the important aspects of the reliable software. For this reason, software practitioners are interested in the methods that enable to predict defect with software metrics. Most studies focus on what kind of techniques would be fit for the prediction model; however, the research about what is the appropriate number of software metrics for building effective defect prediction model are relatively infrequent. In this paper, we propose a new approach for selecting a subset of software metrics by exploiting the correlation structure among them. We construct software metrics networks with two different relationship measures of MIC (Maximal Information Coefficient) and Pearson correlation coefficient. We then adjust the threshold for filtering out edges with weak correlation, and select representative features for each of the resulting connected components. We validate this subset selection approach with the Poisson regression model. Our results demonstrate that metrics selection using networks with relationship coefficient is efficient enough compared to model with the full set of features.

Current software quality estimation models often involve the use of supervised learning methods for building a software fault prediction models. In such models, dependent variable usually represents a software quality measurement indicating the quality of a module by risk-basked class membership, or the number of faults. Independent variables include various software metrics as McCabe, Error Count, Halstead, Line of Code, etc... In this paper we present the use of advanced tool for data mining called Multimethod on the case of building software fault prediction model. Multimethod combines different aspects of supervised learning methods in dynamical environment and therefore can improve accuracy of generated prediction model. We demonstrate the use Multimethod tool on the real data from the Metrics Data Project Data (MDP) Repository. Our preliminary empirical results show promising potentials of this approach in predicting software quality in a software measurement and quality dataset.

Software metrics provide a quantitative basis for planning and predicting software development processes. Therefore the quality of software can be controlled and improved easily. Quality in fact aids higher productivity, which has brought software metrics to the forefront. This research paper focuses on different views on software quality. Moreover, many metrics and models have been developed; promoted and utilized resulting in remarkable successes. This paper examines the realm of software engineering to see why software metrics are needed and also reviews their contribution to software quality and reliability. Results can be improved further as we acquire additional experience with variety of software metrics. These experiences can yield tremendous benefits and betterment in quality and reliability.

Summary form only given. The primary objective of introducing metrics and measurement is to improve software quality, and thus the productivity of the organization. Implementation of a software quality measurement effort, however, is not without cost. Therefore, it is important to understand that introducing software metrics and measurement may result in increasing up-front costs. Some consideration should be given to performing a cost benefit analysis to help determine if expectations are realistic and if the predefined goals are feasible. The identification of desired outcomes/results and the selection of appropriate measurement criteria are key to the success of a software improvement program.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

With the rapid development of computer hardware and software technology, the hardware size and performance of embedded system have been greatly improved, accordingly, the complexity and size of the embedded system software have also doubled.However, the particularity of the embedded system requires the hardware system and application software operated on it to be concise and efficient, stable and reliable.This makes the proportion of the software development in the whole embedded system development higher and higher.The software quality plays a crucial role in the whole product quality .As a result, we urgently need a testing method and tools for embedded field to improve software quality and reliability, shorten the software development period and reduce development costs.Under this background, this paper goes into the testing technology of embedded electronic system software and applies the research results to testing.From the actual needs, it goes into the software metrics technology and applies it to vehicle electronic system testing.