Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.
OSS projects are dynamic in nature. Developers contribute to a project for a certain period of time and later leave the project or join other projects of high interest. Hence, the OSS community always welcomes members who can attain the role of a developer in a project. In this paper, we investigate contributions made by members who have attained the role of a developer. In particular, we study the contributions made by the members in terms of bugs reported, comments on bugs, source-code patch submissions, and their social relation with other members of an OSS community. Further, we study the significance of nondevelopers contribution and investigate if and to what extent they play a role in the long-term survival of an OSS project. Moreover, we investigate the ratio of contributions made by a member before and after attaining the role of a developer. We have outlined 4 research questions in this regard and further discuss our findings based on the research questions by taking into account data from software repositories of 4 different Apache projects.
Several studies have been proposed in the literature on software clones from different points of view and covering many correlated features and areas, which are particularly relevant to software maintenance and evolution. In this paper, we describe our experience on clone detection through three different tools and investigate the impact of clone refactoring on different software quality metrics.
The cloud computing paradigm has brought the benefits of utility computing to a global scale. It has gained paramount attention in recent years. Companies are seriously considering to adopt this new paradigm and expecting to receive significant benefits. In fact, the concept of cloud computing is not a revolution in terms of technology; it has been established based on the solid ground of virtualization, distributed system, and web services. To comprehend cloud computing, its foundations and technological landscape need to be adequately understood. This paper provides a comprehensive review on the building blocks of cloud computing and relevant technological aspects. It focuses on four key areas including architecture, virtualization, data management, and security issues.
Feedback for student programming assignments on quality is a tedious and laborious task for the instructor. In this paper, we make use of few object-oriented software metrics along with a reference code that is provided by the instructor to analyze student programs and provide feedback. The empirical study finds those software metrics that can be used on the considered programming assignments and the way reference code helps the instructor to assess them. This approach helps the instructor to easily find out quality issues in student programs. Feedback to such assignments can be provided using the guidelines which we will be discussing. We also perform an experimental study on programming assignments of sophomore students who were enrolled in an object-oriented programming course to validate our approach.
Whilst several agent-oriented modelling languages have been developed by independent research groups, it is now appropriate to consider a consolidation of these various approaches. There are arguably three things that need consolidation and future standardization: individual symbols, the underpinning metamodel, and the diagram types. Here we address only the third issue by extending an earlier analysis that resulted in recommendations for various diagram types for the modelling of a multiagent system (MAS). Here, we take each of these previously recommended diagram types and see how each is realized in a wide variety (over 20) of current agent-oriented software engineering (AOSE) methodologies. We also take the opportunity to express, as exemplars, some of these diagram types using the recently published FAML notation.
Business Process Management (BPM) includes methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. It can be considered as an extension of classical Workflow Management (WFM) systems and approaches. Although the practical relevance of BPM is undisputed, a clear definition of BPM and related acronyms such as BAM, BPA, and STP are missing. Moreover, a clear scientific foundation is missing. In this paper, we try to demystify the acronyms in this domain, describe the state-of-the-art technology, and argue that BPM could benefit from formal methods/languages (cf. Petri nets, process algebras, etc.).
The wide availability of high-performance computing systems, Grids and Clouds, allowed scientists and engineers to implement more and more complex applications to access and process large data repositories and run scientific experiments in silico on distributed computing platforms. Most of these applications are designed as workflows that include data analysis, scientific computation methods, and complex simulation techniques. Scientific applications require tools and high-level mechanisms for designing and executing complex workflows. For this reason, in the past years, many efforts have been devoted towards the development of distributed workflow management systems for scientific applications. This paper discusses basic concepts of scientific workflows and presents workflow system tools and frameworks used today for the implementation of application in science and engineering on high-performance computers and distributed systems. In particular, the paper reports on a selection of workflow systems largely used for solving scientific problems and discusses some open issues and research challenges in the area.
Software maintenance is an important activity in software development. Some development methodologies such as the object-oriented have contributed in improving maintainability of software. However, crosscutting concerns are still challenges that affect the maintainability of OO software. In this paper, we discuss our case study to assess the extent of maintainability improvement that can be achieved by employing aspect-oriented programming. Aspect-oriented programming (AOP) is a relatively new approach that emphasizes dealing with crosscutting concerns. To demonstrate the maintainability improvement, we refactored a COTS-based system known as OpenBravoPOS using AspectJ and compared its maintainability with the original OO version. We used both structural complexity and concern level metrics. Our results show an improvement of maintainability in the AOP version of OpenBravoPOS.
Coordination models and languages are meant to provide abstractions and mechanisms to harness the space of interaction as one of the foremost sources of complexity in computational systems. Nature-inspired computing aims at understanding the mechanisms and patterns of complex natural systems in order to bring their most desirable features to computational systems. Thus, the promise of nature-inspired coordination models is to prove themselves fundamental in the design of complex computational systems|such as intelligent, knowledge-intensive, pervasive, adaptive, and self-organising ones. In this paper, we survey the most relevant nature-inspired coordination models in the literature, focussing in particular on tuple-based models, and foresee the most interesting research trends in the field.
The purpose of this paper is to introduce the motivations behind end user development, discuss its basic concepts and roots, and review the current state of art. Various approaches are discussed and classified in terms of their main features and the technologies and platforms for which they have been developed. Lastly, the paper provides an indication of interesting possibilities for further evolution.
Internet of Services (IoS) vision allows users to allocate and consume different web services on the fly without any prior knowledge regarding the chosen services. Such chosen services should automatically interact with one another in a transparent manner to accomplish the required users' goals. As services are chosen on the fly, service conversations are not necessarily compatible due to incompatibilities between services signatures and/or conversation protocols, creating obstacles for realizing the IoS vision. One approach for overcoming this problem is to use conversation adapters. However, such conversion adapters must be automatically created on the fly as chosen services are only known at run time. Existing approaches for automatic adapter generation are syntactic and very limited; hence they cannot be adopted in such dynamic environments. To overcome such limitation, this paper proposes a novel approach for automatic adapter generation that uses conditional substitution semantics between application domain concepts and operations to automatically generate the adapter conversion functions. Such conditional substitution semantics are captured using a concepts substitutability enhanced graph required to be part of application domain ontologies. Experiments results show that the proposed approach provides more accurate conversation adaptation results when compared against existing syntactic adapter generation approaches.
Control flow graphs are a well-known graphical representation of programs that capture the control flow but abstract from program details. In this paper, we derive decision graphs that reduce control flow graphs but preserve the branching structure of programs. As an application to software engineering, we use decision graphs to compare and clarify different definitions of branch covering in software testing.
Context . Software metrics are surrogates of software quality. Software metrics can be used to find possible problems or chances for improvements in software quality. However, software metrics are numbers that are not easy to interpret. Previous analysis of software metrics has shown fat tails in the distribution. The skewness and fat tails of such data are properties of many statistical distributions and more importantly the phenomena of the power law. These statistical properties affect the interpretation of software quality metrics. Objectives . The objective of this research is to validate the effect of power laws on the interpretation of software metrics. Method . To investigate the effect of power law properties on software quality, we study five open-source systems to investigate the distribution and their effect on fault prediction models. Results . Study shows that power law behavior has an effect on the interpretation and usage of software metrics and in particular the CK metrics. Many metrics have shown a power law behavior. Threshold values are derived from the properties of the power law distribution when applied to open-source systems. Conclusion . The properties of a power law distribution can be effective in improving the fault-proneness models by setting reasonable threshold values.
Software developers require information to understand the characteristics of systems, such as complexity and maintainability. In order to further understand and determine characteristics of object-oriented (OO) systems, this paper describes research that identifies attributes that are valuable in determining the difficulty in implementing changes during maintenance, as well as the possible effects that such changes may produce. A set of metrics are proposed to quantify and measure these attributes. The proposed complexity metrics are used to determine the difficulty in implementing changes through the measurement of method complexity, method diversity, and complexity density. The paper establishes impact metrics to determine the potential effects of making changes to a class and dependence metrics that are used to measure the potential effects on a given class resulting from changes in other classes. The case study shows that the proposed metrics provide additional information not sufficiently provided by the related existing OO metrics. The metrics are also found to be useful in the investigation of large systems, correlating with project outcomes.
Software developers use various software repositories in order to interact with each other or to solve related problems. These repositories provide a rich source of information for a wide range of tasks. However, one issue to overcome in order to make this information useful is the identification and interlinking of multiple identities of developers. In this paper, we propose a Linked Data-based methodology to interlink and integrate multiple identities of a developer found in different software repositories of a project as well as across repositories of multiple projects. Providing such interlinking will enable us to keep track of a developer’s activity not only within a single project but also across multiple projects. The methodology will be presented in general and applied to 5 Apache projects as a case study. Further, we show that the few methods suggested so far are not always appropriate to overcome the developer identification problem.
This paper presents a regression testing technique (and associated tool) for object-oriented software based on use cases. The basic models we use to describe use cases are UML statechart and collaboration diagrams. The technique combines, in fact, the analysis of these models to a simple static analysis of the code. The goal is to identify changes that may not be visible in design models (change in a method’s body). The developed tool identifies modified (impacted by modifications) use cases and selects the appropriate test cases from an existing test suite. New test cases are generated when necessary. The selected test cases (including the new ones) are automatically executed. A case study is reported to provide evidence of the feasibility of the approach and its benefits (reduction of regression testing effort).
Characterising for contemporary systems is their dependence on constituent systems to provide information, functionality, and scalability. Moreover, as the tasks that systems perform are ever more intimate and critical in their nature, reliability and correctness are great concerns. On these matters, we outline a methodology for formal integration of systems. We claim this formal approach to assist in managing the complexity and correctness, in preserving reliability and in respecting the independence of the constituent systems. As a proof of concept, we integrate two in-house control systems specified independently in the Event-B language with the Rodin Platform tool. Moreover, we show how to introduce a new functionality that is only possible due to the integration. Hence, we formally construct a system of systems and provide the methods for hierarchical integration of those.
We present our approach to real-time service-oriented scheduling problems with the objective of maximizing the total system utility. Different from the traditional utility accrual scheduling problems that each task is associated with only a single time utility function (TUF), we associate two different TUFs—a profit TUF and a penalty TUF—with each task, to model the real-time services that not only need to reward the early completions but also need to penalize the abortions or deadline misses. The scheduling heuristics we proposed in this paper judiciously accept, schedule, and abort real-time services when necessary to maximize the accrued utility. Our extensive experimental results show that our proposed algorithms can significantly outperform the traditional scheduling algorithms such as the Earliest Deadline First (EDF), the traditional utility accrual (UA) scheduling algorithms, and an earlier scheduling approach based on a similar model.
In this research, a hybrid cost estimation model is proposed to produce a realistic prediction model that takes into consideration software project, product, process, and environmental elements. A cost estimation dataset is built from a large number of open source projects. Those projects are divided into three domains: communication, finance, and game projects. Several data mining techniques are used to classify software projects in terms of their development complexity. Data mining techniques are also used to study association between different software attributes and their relation to cost estimation. Results showed that finance metrics are usually the most complex in terms of code size and some other complexity metrics. Results showed also that games applications have higher values of the SLOCmath, coupling, cyclomatic complexity, and MCDC metrics. Information gain is used in order to evaluate the ability of object-oriented metrics to predict software complexity. MCDC metric is shown to be the first metric in deciding a software project complexity. A software project effort equation is created based on clustering and based on all software projects’ attributes. According to the software metrics weights values developed in this project, we can notice that MCDC, LOC, and cyclomatic complexity of the traditional metrics are still the dominant metrics that affect our classification process, while number of children and depth of inheritance are the dominant from the object-oriented metrics as a second level.
