An empirical study of testing and integration strategies using artificial software systems

Software architecture is an artifact that expresses how the initial concept of a software system has actually been implemented. However, changes to the requirement imply continuous modification of the software system and may affect its architecture. It is expected that when a software system reaches the mature state, the requirements for evolution decrease and its architecture becomes more stable. The paper analyzes how the architecture of a software system evolves during its life cycle, with the aim of obtaining quantitative information on its possible instability after it has been declared mature. The goal is to verify if the architectural instability decreases with the increase of the software system maturity and to identify the software components that are more unstable among multiple releases. The paper proposes metrics that measure the instability of the architecture of a software system and its components through different releases. Open source software projects classified as mature and active and related historical data are analyzed. The results of the empirical study point out that the instability of software projects continues to evolve even after they are declared mature. The proposed metrics give a useful support for investigating the instability of a software project, even if further factors can be analyzed. Furthermore, the study can be replicated on other software systems belonging to different domains and developed using different programming languages.

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An Empirical Study of Technical Debt in Open-Source Software Systems

Disciplinary Convergence in Systems Engineering Research ◽

10.1007/978-3-319-62217-0_9 ◽

2017 ◽

pp. 113-125 ◽

Cited By ~ 1

Author(s):

Reem Alfayez ◽

Celia Chen ◽

Pooyan Behnamghader ◽

Kamonphop Srisopha ◽

Barry Boehm

Keyword(s):

Empirical Study ◽

Open Source ◽

Open Source Software ◽

Software Systems ◽

Technical Debt

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An empirical study to investigate the efficacy of collaborative immersive virtual reality systems for designing information architecture of software systems

Applied Ergonomics ◽

10.1016/j.apergo.2019.05.009 ◽

2019 ◽

Vol 80 ◽

pp. 175-186 ◽

Cited By ~ 1

Author(s):

Shraddhaa Narasimha ◽

Emma Dixon ◽

Jeffrey W. Bertrand ◽

Kapil Chalil Madathil

Keyword(s):

Virtual Reality ◽

Empirical Study ◽

Information Architecture ◽

Software Systems ◽

Immersive Virtual Reality

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Use of software systems development methods An empirical study in Brunei Darussalam

Information and Software Technology ◽

10.1016/s0950-5849(97)00052-9 ◽

1998 ◽

Vol 39 (14-15) ◽

pp. 949-963 ◽

Cited By ~ 9

Author(s):

Md.Mahbubur Rahim ◽

Afzaal H. Seyal ◽

Mohd.Noah Abd. Rahman

Keyword(s):

Empirical Study ◽

Systems Development ◽

Software Systems ◽

Brunei Darussalam ◽

Development Methods

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METRIC SELECTION FOR SOFTWARE DEFECT PREDICTION

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194011005256 ◽

2011 ◽

Vol 21 (02) ◽

pp. 237-257 ◽

Cited By ~ 15

Author(s):

HUANJING WANG ◽

TAGHI M. KHOSHGOFTAAR ◽

JASON VAN HULSE ◽

KEHAN GAO

Keyword(s):

Empirical Study ◽

Prediction Models ◽

Characteristic Curve ◽

Life Cycles ◽

Defect Prediction ◽

Software Systems ◽

Software Projects ◽

Ensemble Technique ◽

Defect Prediction Models ◽

Software Practitioners

Real-world software systems are becoming larger, more complex, and much more unpredictable. Software systems face many risks in their life cycles. Software practitioners strive to improve software quality by constructing defect prediction models using metric (feature) selection techniques. Finding faulty components in a software system can lead to a more reliable final system and reduce development and maintenance costs. This paper presents an empirical study of six commonly used filter-based software metric rankers and our proposed ensemble technique using rank ordering of the features (mean or median), applied to three large software projects using five commonly used learners. The classification accuracy was evaluated in terms of the AUC (Area Under the ROC (Receiver Operating Characteristic) Curve) performance metric. Results demonstrate that the ensemble technique performed better overall than any individual ranker and also possessed better robustness. The empirical study also shows that variations among rankers, learners and software projects significantly impacted the classification outcomes, and that the ensemble method can smooth out performance.

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