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Simona Motogna - One of the best experts on this subject based on the ideXlab platform.

  • evaluation of Software product quality Metrics
    International Conference on Evaluation of Novel Approaches to Software Engineering, 2019
    Co-Authors: Arthurjozsef Molnar, Alexandra Neamţu, Simona Motogna
    Abstract:

    Computing devices and associated Software govern everyday life, and form the backbone of safety critical systems in banking, healthcare, automotive and other fields. Increasing system complexity, quickly evolving technologies and paradigm shifts have kept Software quality research at the forefront. Standards such as ISO’s 25010 express it in terms of sub-characteristics such as maintainability, reliability and security. A significant body of literature attempts to link these subcharacteristics with Software Metric values, with the end goal of creating a Metric-based model of Software product quality. However, research also identifies the most important existing barriers. Among them we mention the diversity of Software application types, development platforms and languages. Additionally, unified definitions to make Software Metrics truly language-agnostic do not exist, and would be difficult to implement given programming language levels of variety. This is compounded by the fact that many existing studies do not detail their methodology and tooling, which precludes researchers from creating surveys to enable data analysis on a larger scale. In our paper, we propose a comprehensive study of Metric values in the context of three complex, open-source applications. We align our methodology and tooling with that of existing research, and present it in detail in order to facilitate comparative evaluation. We study Metric values during the entire 18-year development history of our target applications, in order to capture the longitudinal view that we found lacking in existing literature. We identify Metric dependencies and check their consistency across applications and their versions. At each step, we carry out comparative evaluation with existing research and present our results.

Arthurjozsef Molnar - One of the best experts on this subject based on the ideXlab platform.

  • evaluation of Software product quality Metrics
    International Conference on Evaluation of Novel Approaches to Software Engineering, 2019
    Co-Authors: Arthurjozsef Molnar, Alexandra Neamţu, Simona Motogna
    Abstract:

    Computing devices and associated Software govern everyday life, and form the backbone of safety critical systems in banking, healthcare, automotive and other fields. Increasing system complexity, quickly evolving technologies and paradigm shifts have kept Software quality research at the forefront. Standards such as ISO’s 25010 express it in terms of sub-characteristics such as maintainability, reliability and security. A significant body of literature attempts to link these subcharacteristics with Software Metric values, with the end goal of creating a Metric-based model of Software product quality. However, research also identifies the most important existing barriers. Among them we mention the diversity of Software application types, development platforms and languages. Additionally, unified definitions to make Software Metrics truly language-agnostic do not exist, and would be difficult to implement given programming language levels of variety. This is compounded by the fact that many existing studies do not detail their methodology and tooling, which precludes researchers from creating surveys to enable data analysis on a larger scale. In our paper, we propose a comprehensive study of Metric values in the context of three complex, open-source applications. We align our methodology and tooling with that of existing research, and present it in detail in order to facilitate comparative evaluation. We study Metric values during the entire 18-year development history of our target applications, in order to capture the longitudinal view that we found lacking in existing literature. We identify Metric dependencies and check their consistency across applications and their versions. At each step, we carry out comparative evaluation with existing research and present our results.

Motogna Simona - One of the best experts on this subject based on the ideXlab platform.

  • Longitudinal Evaluation of Open-Source Software Maintainability
    2020
    Co-Authors: Molnar Arthur-jozsef, Motogna Simona
    Abstract:

    We present a longitudinal study on the long-term evolution of maintainability in open-source Software. Quality assessment remains at the forefront of both Software research and practice, with many models and assessment methodologies proposed and used over time. Some of them helped create and shape standards such as ISO 9126 and 25010, which are well established today. Both describe Software quality in terms of characteristics such as reliability, security or maintainability. An important body of research exists linking these characteristics with Software Metrics, and proposing ways to automate quality assessment by aggregating Software Metric values into higher-level quality models. We employ the Maintainability Index, technical debt ratio and a maintainability model based on the ARiSA Compendium. Our study covers the entire 18 year development history and all released versions for three complex, open-source applications. We determine the maintainability for each version using the proposed models, we compare obtained results and use manual source code examination to put them into context. We examine the common development patterns of the target applications and study the relation between refactoring and maintainability. Finally, we study the strengths and weaknesses of each maintainability model using manual source code examination as the baseline

  • Evaluation of Software Product Quality Metrics
    'Springer Science and Business Media LLC', 2020
    Co-Authors: Molnar Arthur-jozsef, Neamţu Alexandra, Motogna Simona
    Abstract:

    Computing devices and associated Software govern everyday life, and form the backbone of safety critical systems in banking, healthcare, automotive and other fields. Increasing system complexity, quickly evolving technologies and paradigm shifts have kept Software quality research at the forefront. Standards such as ISO's 25010 express it in terms of sub-characteristics such as maintainability, reliability and security. A significant body of literature attempts to link these subcharacteristics with Software Metric values, with the end goal of creating a Metric-based model of Software product quality. However, research also identifies the most important existing barriers. Among them we mention the diversity of Software application types, development platforms and languages. Additionally, unified definitions to make Software Metrics truly language-agnostic do not exist, and would be difficult to implement given programming language levels of variety. This is compounded by the fact that many existing studies do not detail their methodology and tooling, which precludes researchers from creating surveys to enable data analysis on a larger scale. In our paper, we propose a comprehensive study of Metric values in the context of three complex, open-source applications. We align our methodology and tooling with that of existing research, and present it in detail in order to facilitate comparative evaluation. We study Metric values during the entire 18-year development history of our target applications, in order to capture the longitudinal view that we found lacking in existing literature. We identify Metric dependencies and check their consistency across applications and their versions. At each step, we carry out comparative evaluation with existing research and present our results.Comment: Published in: Molnar AJ., Neam\c{t}u A., Motogna S. (2020) Evaluation of Software Product Quality Metrics. In: Damiani E., Spanoudakis G., Maciaszek L. (eds) Evaluation of Novel Approaches to Software Engineering. ENASE 2019. Communications in Computer and Information Science, vol 1172. Springer, Cham. https://doi.org/10.1007/978-3-030-40223-5_

Alexandra Neamţu - One of the best experts on this subject based on the ideXlab platform.

  • evaluation of Software product quality Metrics
    International Conference on Evaluation of Novel Approaches to Software Engineering, 2019
    Co-Authors: Arthurjozsef Molnar, Alexandra Neamţu, Simona Motogna
    Abstract:

    Computing devices and associated Software govern everyday life, and form the backbone of safety critical systems in banking, healthcare, automotive and other fields. Increasing system complexity, quickly evolving technologies and paradigm shifts have kept Software quality research at the forefront. Standards such as ISO’s 25010 express it in terms of sub-characteristics such as maintainability, reliability and security. A significant body of literature attempts to link these subcharacteristics with Software Metric values, with the end goal of creating a Metric-based model of Software product quality. However, research also identifies the most important existing barriers. Among them we mention the diversity of Software application types, development platforms and languages. Additionally, unified definitions to make Software Metrics truly language-agnostic do not exist, and would be difficult to implement given programming language levels of variety. This is compounded by the fact that many existing studies do not detail their methodology and tooling, which precludes researchers from creating surveys to enable data analysis on a larger scale. In our paper, we propose a comprehensive study of Metric values in the context of three complex, open-source applications. We align our methodology and tooling with that of existing research, and present it in detail in order to facilitate comparative evaluation. We study Metric values during the entire 18-year development history of our target applications, in order to capture the longitudinal view that we found lacking in existing literature. We identify Metric dependencies and check their consistency across applications and their versions. At each step, we carry out comparative evaluation with existing research and present our results.

Jun Chen - One of the best experts on this subject based on the ideXlab platform.

  • an empirical study on Software defect prediction with a simplified Metric set
    Information & Software Technology, 2015
    Co-Authors: Xiao Liu, Jun Chen
    Abstract:

    ContextSoftware defect prediction plays a crucial role in estimating the most defect-prone components of Software, and a large number of studies have pursued improving prediction accuracy within a project or across projects. However, the rules for making an appropriate decision between within- and cross-project defect prediction when available historical data are insufficient remain unclear. ObjectiveThe objective of this work is to validate the feasibility of the predictor built with a simplified Metric set for Software defect prediction in different scenarios, and to investigate practical guidelines for the choice of training data, classifier and Metric subset of a given project. MethodFirst, based on six typical classifiers, three types of predictors using the size of Software Metric set were constructed in three scenarios. Then, we validated the acceptable performance of the predictor based on Top-k Metrics in terms of statistical methods. Finally, we attempted to minimize the Top-k Metric subset by removing redundant Metrics, and we tested the stability of such a minimum Metric subset with one-way ANOVA tests. ResultsThe study has been conducted on 34 releases of 10 open-source projects available at the PROMISE repository. The findings indicate that the predictors built with either Top-k Metrics or the minimum Metric subset can provide an acceptable result compared with benchmark predictors. The guideline for choosing a suitable simplified Metric set in different scenarios is presented in Table 12. ConclusionThe experimental results indicate that (1) the choice of training data for defect prediction should depend on the specific requirement of accuracy; (2) the predictor built with a simplified Metric set works well and is very useful in case limited resources are supplied; (3) simple classifiers (e.g., Naive Bayes) also tend to perform well when using a simplified Metric set for defect prediction; and (4) in several cases, the minimum Metric subset can be identified to facilitate the procedure of general defect prediction with acceptable loss of prediction precision in practice.