Open-source Object-oriented Software Research Articles

Neural Network-Based Model for the Quality Assessment of Object-Oriented Software

Software quality assessment is an important subject among the researchers in the software development domain. The quality assessment is generally done either at the design level through some of the design attributes or through code when the product is ready. These two types of software quality are referred to as design quality and product quality, respectively. Several techniques and tools are available that facilitate to assess the design as well as the product quality of software. In this paper, a neural network model is proposed for the assessment of quality of object-oriented software at the product level. The authors select a subset of existing object-oriented metrics that are normalized at three levels and used to find quality factors like understandability, reusability, flexibility, maintainability, reliability, extensibility, and modifiability for the model development. The model is validated by assessing quality levels of 33 open source object-oriented software of different design complexities and observing a high correlation between these quality levels in comparison with an existing model.

Current Distribution Modeling in the Open-Source OPENSC2 Tool for the Multi-Physics Analysis of HTS and LTS Cables

In the framework of Open Science, the OPENSC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> open-source object-oriented software for multi-physics analysis has been developed to simulate the operating transients in forced-flow superconducting cables for fusion and power applications. Due to high magnetic fields and transport currents involved, the dissipated power by Joule effect and AC losses can compromise the thermal stability of the superconductors, so that the electromagnetic problem comes intrinsically with the thermal-hydraulic one. The numerical current distribution model, recently developed for OPENSC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , is reported here. After the solution verification and validation, followed by benchmarks against other software and experimental data, a first coupling with the thermal-hydraulic transients has been tested and reported in this work.

QUALITY OF SOFTWARE PROJECTS – A CASE STUDY

In software projects, many points that are overlooked such as time constraints and human factors are causing great problems in the future. By measuring the quality of software projects, problems that may arise in important parameters such as maintenance-repair, functionality and reliability can be eliminated. In this study, metrics that can be used for measuring maintainability quality attribute within the scope of ISO 9126 Quality Standard are examined. In order to perform the study, 40 open source object-oriented software was selected and code complexity analysis was performed. Values of metric sets such as Chidamber and Kemerer (CK), Lorenz and Kidd (LK) and McCabe's complex Suite were determined by the Understand Code Analysis tool. It was determined whether the obtained values exceeded the threshold values indicated in the literature. Frequencies of metrics passing threshold values were determined for 40 open source object-oriented software projects, and the consistency among the metrics was evaluated using WEKA Machine Learning Software and EXCEL Data Analysis Tool. When the results were evaluated, it was observed that in addition to CK metrics such as WMC, CBO, and RFC, which measure the maintainability quality attribute, NOC (CK), NIM (LK), and the ratio of comment/code metrics have been observed to yield significant measurement results.

Sensitivity Analysis of Evolutionary Algorithm for Software Reusability

Fast and competitive software industry demands rapid development using Component Based Software Development (CBSD). CBSD is dependent on the availability of the high-quality reusable component libraries. Recently, evolutionary multi-objective optimization algorithms have been used to identify sets of reusable software components from the source-code of Object Oriented (OO) software, using different quality indicators (e.g. cohesion, coupling, etc.). Sometimes, these used quality indicators are quite sensitive towards the small variations in their values, although they should not be. Therefore, this paper analyzes the sensitivity of the evolutionary technique for three quality indicators used during the identification: Frequent Usage Pattern (FUP), Semantic and evolutionary coupling. The sensitivity analysis is performed on three widely used open-source OO software. The experimentation is performed by mutating the system to different degrees. Results of the empirical analysis indicate that the semantic parameter is most sensitive and important. Ignoring this feature highly degrades the quality; FUP relation is uniformly sensitive and evolutionary relations's sensitivity is non-uniform.

Automatic clustering constraints derivation from object-oriented software using weighted complex network with graph theory analysis

Abstract Constrained clustering or semi-supervised clustering has received a lot of attention due to its flexibility of incorporating minimal supervision of domain experts or side information to help improve clustering results of classic unsupervised clustering techniques. In the domain of software remodularisation, classic unsupervised software clustering techniques have proven to be useful to aid in recovering a high-level abstraction of the software design of poorly documented or designed software systems. However, there is a lack of work that integrates constrained clustering for the same purpose to help improve the modularity of software systems. Nevertheless, due to time and budget constraints, it is laborious and unrealistic for domain experts who have prior knowledge about the software to review each and every software artifact and provide supervision on an on-demand basis. We aim to fill this research gap by proposing an automated approach to derive clustering constraints from the implicit structure of software system based on graph theory analysis of the analysed software. Evaluations conducted on 40 open-source object-oriented software systems show that the proposed approach can serve as an alternative solution to derive clustering constraints in situations where domain experts are non-existent, thus helping to improve the overall accuracy of clustering results.

Analysis of Composite Steel-concrete Beams Exposed to Fire using OpenSees

OpenSees is an open-source object-oriented software framework developed at UC Berekeley. The OpenSees framework has been recently extended to deal with structural behaviour under fire conditions. This paper summaries the key work done for this extension and focuses on the validation and application of the developed OpenSees to study the behaviour of composite steel-concrete beams under fire conditions. The performance of the developed OpenSees are verified by four mechanical tests and two fire tests on simply supported composite beams. A parametric study is carried out using OpenSees to study the influence of boundary condition as well as composite effect of slab on the behavior of composite beams exposed to fire. The stress and strain along the beam section is output and compared with yield stress limit at elevated temperature to explain these influences in detail. The results show that the stress distribution in the web of the steel beam is more complex due to the support effects.

An empirical analysis of package-modularization metrics: Implications for software fault-proneness

ContextIn a large object-oriented software system, packages play the role of modules which group related classes together to provide well-identified services to the rest of the system. In this context, it is widely believed that modularization has a large influence on the quality of packages. Recently, Sarkar, Kak, and Rama proposed a set of new metrics to characterize the modularization quality of packages from important perspectives such as inter-module call traffic, state access violations, fragile base-class design, programming to interface, and plugin pollution. These package-modularization metrics are quite different from traditional package-level metrics, which measure software quality mainly from size, extensibility, responsibility, independence, abstractness, and instability perspectives. As such, it is expected that these package-modularization metrics should be useful predictors for fault-proneness. However, little is currently known on their actual usefulness for fault-proneness prediction, especially compared with traditional package-level metrics. ObjectiveIn this paper, we examine the role of these new package-modularization metrics for determining software fault-proneness in object-oriented systems. MethodWe first use principal component analysis to analyze whether these new package-modularization metrics capture additional information compared with traditional package-level metrics. Second, we employ univariate prediction models to investigate how these new package-modularization metrics are related to fault-proneness. Finally, we build multivariate prediction models to examine the ability of these new package-modularization metrics for predicting fault-prone packages. ResultsOur results, based on six open-source object-oriented software systems, show that: (1) these new package-modularization metrics provide new and complementary views of software complexity compared with traditional package-level metrics; (2) most of these new package-modularization metrics have a significant association with fault-proneness in an expected direction; and (3) these new package-modularization metrics can substantially improve the effectiveness of fault-proneness prediction when used with traditional package-level metrics together. ConclusionsThe package-modularization metrics proposed by Sarkar, Kak, and Rama are useful for practitioners to develop quality software systems.

Progressive Collapse Mechanisms of Steel Frames Exposed to Fire

OpenSees is an open-source object-oriented software framework developed at UC Berekeley. The OpenSees framework has been recently extended to deal with structural behavior under fire conditions. This paper summaries the key work done for this extension and focuses on the application of the developed OpenSees to study the fire-induced progressive collapse mechanisms of steel structures. The implicit dynamic analysis method (Newmark method) is applied and the influences of the load ratios, beam sizes and fire scenarios on the collapse behavior of frames are investigated. Single-compartment fire scenarios in the central bay and edge bay are considered, respectively. A total of four collapse mechanisms of steel frames are proposed by varying the three influencing factors. Most of the collapse of steel frames is triggered by the buckling of the heated columns. The thermal expansion of heated beams at early heating stage and their catenary action at high temperature have great influences on the collapse mechanisms. The most common collapse mode of steel frames are in the form of lateral drift of frames above the heated floor together with downward collapse of frames along the heated bay. As the load increases, the collapse behavior of structures is dominated by a downward collapse of the whole frame with little sign of the upper frame drift. The collapse modes of steel frames with strong and weak beams are column failure mechanism and beam failure mechanism, respectively. The former mechanism is due to the buckling of the columns below the heated floor represented by a global collapse of the frame and the latter is initiated by the premature development of plastic hinges at the ends of beams denoted by an obvious lateral drift of the heated floor. Generally, the edge bay fire is more prone to induce the collapse of structures than the central bay fire. It is found that the most dangerous situation is the frame subjected to high load ratios exposed to a central bay fire where its progressive collapse may occur as early as 250°C.

A Hybrid Set of Complexity Metrics for Large-Scale Object-Oriented Software Systems

Large-scale object-oriented (OO) software systems have recently been found to share global network characteristics such as small world and scale free, which go beyond the scope of traditional software measurement and assessment methodologies. To measure the complexity at various levels of granularity, namely graph, class (and object) and source code, we propose a hierarchical set of metrics in terms of coupling and cohesion — the most important characteristics of software, and analyze a sample of 12 open-source OO software systems to empirically validate the set. Experimental results of the correlations between cross-level metrics indicate that the graph measures of our set complement traditional software metrics well from the viewpoint of network thinking, and provide more effective information about fault-prone classes in practice.