Test suite effectiveness: an indicator for open source software quality

Organizations are becoming increasingly concerned about software quality. In object-oriented (OO) systems, quality is characterized by measurements of internal quality attributes. An efficient and proper method to analyze software quality in the absence of fault-prone or defective data labels is cluster analysis. The aim of this paper is to find similarities among project structures by measuring characteristics of internal software quality. In a sample of 150 open-source software systems, we evaluated software using macro and micro categories. Results obtained using cluster analysis indicated that some domains such as Graphics, Games, and Development tend to have similarities in specialization, abstraction, stability, and complexity. These results exploit the ability of OO software metrics to find similar behavior across domains. The results provide an immediate view of the trends and characteristics of internal software quality of Java systems that need to be addressed so that software systems can continue to be maintainable.

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Trustworthiness of collaborative Open Source Software quality assessment

2007 Third International Conference on Security and Privacy in Communications Networks and the Workshops - SecureComm 2007 ◽

10.1109/seccom.2007.4550301 ◽

2007 ◽

Author(s):

Jean-Marc Seigneur

Keyword(s):

Quality Assessment ◽

Open Source ◽

Software Quality ◽

Open Source Software

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A Quantitative Study for the Characterization of Internal Quality of Open-Source Object-Oriented Software

10.5753/sbqs.2016.15150 ◽

2016 ◽

Author(s):

Mariana De Azevedo Santos ◽

Paulo Henrique De Souza Bermejo ◽

Heitor Costa

Keyword(s):

Open Source ◽

Software Quality ◽

Open Source Software ◽

Object Oriented ◽

Internal Quality ◽

Development Lifecycle ◽

Study Results ◽

Software Development Lifecycle ◽

Software Quality Models

Although it is necessary, activities regarding quality assurance and maintenance of software are considered the longest and most complex in software development lifecycle. Taking advantage of this growing trend and of the benefits obtained from open-source initiative, researches on open-source software quality and maintainability have gained renewed interest. The use of robust statistical techniques, such as PLS-SEM to investigate and empirically validate software quality models has also been an efficient alternative to obtain information on open-source software quality. The aim of this study was evaluate and build a conceptual model to characterize the internal quality in Java open-source software in different domains, validated with the PLS-SEM technique. The study results indicate that there are domains with similarities among them and four factors can influence the internal quality of object-oriented software to present better maintainability (Complexity Reduce, Normalized Cohesion, Non-normalized Cohesion, and Increase of the Modularity Level). Besides, we identified some measures are more effective to evaluate internal quality in object-oriented open-source, such as, Fan-out (FOUT), Lack of Cohesion of Methods 2 (LCOM2), Response for Class (RFC), Tight Class Cohesion (TCC), and Loose Class Cohesion (LCC). Thus, this study aims at supporting software engineers and project managers to develop measurement strategies to ensure internal quality of source code and reduce maintenance costs.

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Integrated Metrics Handling in Open Source Software Quality Management Platforms

Advances in Intelligent Systems and Computing - Information Technolog: New Generations ◽

10.1007/978-3-319-32467-8_45 ◽

2016 ◽

pp. 509-518 ◽

Cited By ~ 2

Author(s):

Julio Escribano-Barreno ◽

Javier García-Muñoz ◽

Marisol García-Valls

Keyword(s):

Quality Management ◽

Open Source ◽

Software Quality ◽

Open Source Software ◽

Software Quality Management

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On Predicting Rediscoveries of Software Defects

10.32920/ryerson.14646261 ◽

2021 ◽

Author(s):

Mefta Sadat

Keyword(s):

Customer Satisfaction ◽

Open Source ◽

Predictive Model ◽

Software Quality ◽

Open Source Software ◽

Large Scale ◽

Software Defects ◽

Enterprise Software ◽

Software Projects ◽

Multiple Clients

The same defect may be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. One solution is forcing clients to install a fix for every defect. However, this approach is economically infeasible, because it requires extra resources and increases downtime. Moreover, it may lead to regression of functionality, as new fixes may break the existing functionality. Our goal is to find a way to proactively predict defects that a client may rediscover in the future. We build a predictive model by leveraging recommender algorithms. We evaluate our approach with extracted rediscovery data from four groups of large-scale open source software projects (namely, Eclipse, Gentoo, KDE, and Libre) and one enterprise software. The datasets contain information about ⇡ 1.33 million unique defect reports over a period of 18 years (1999-2017). Our proposed approach may help in understanding the defect rediscovery phenomenon, leading to improvement of software quality and customer satisfaction.

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