A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity

Bibi, Stamatia; Ampatzoglou, Apostolos; Stamelos, Ioannis

doi:10.1007/978-3-319-39225-7_3

“…The value of the coefficient denotes the degree to which the value (or ranking for Consistency) of the actual reuse is in analogy to the value (or rank) of the assessor. Discriminative power is represented as the ability of the independent variable to classify an asset into meaningful groups (as defined by the values of the dependent variables). The values of the dependent variable have been classified into three mutually exclusive categories (representing low, medium, and high metric values) adopting equal frequency binning . Then, Bayesian classifiers are applied in order to derive estimates regarding the discrete values of the dependent variables.…”

Section: Case Study Designmentioning

confidence: 99%

REI: An integrated measure for software reusability

Zozas

¹

,

Ampatzoglou

²

,

Bibi

³

et al. 2019

J Software Evolu Process

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To capitalize upon the benefits of software reuse, an efficient selection among candidate reusable assets should be performed in terms of functional fitness and adaptability. The reusability of assets is usually measured through reusability indices. However, these do not capture all facets of reusability, such as structural characteristics, external quality attributes, and documentation. In this paper, we propose a reusability index (REI) as a synthesis of various software metrics and evaluate its ability to quantify reuse, based on IEEE Standard on Software MetricsValidity. The proposed index is compared with existing ones through a case study on 80 reusable open-source assets. To illustrate the applicability of the proposed index, we performed a pilot study, where real-world reuse decisions have been compared with decisions imposed by the use of metrics (including REI). The results of the study suggest that the proposed index presents the highest predictive and discriminative power; it is the most consistent in ranking reusable assets and the most strongly correlated to their levels of reuse. The findings of the paper are discussed to understand the most important aspects in reusability assessment (interpretation of results), and interesting implications for research and practice are provided. 1

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“…Wagner modeled the activities during the software maintenance process with their associated outcomes (comment lines, size, and cyclomatic complexity) and estimated the average maintenance effort. Bibi et al employed BNs to model source code quality characteristics of 20 open source Java applications with maintenance process indicators like duration, effort, and production.…”

Section: Related Workmentioning

confidence: 99%

Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules

Chatzimparmpas

¹

,

Bibi

²

2019

J Software Evolu Process

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Managing the maintenance process and estimating accurately the effort and duration required for a new release is considered to be a crucial task as it affects successful software project survival and progress over time. In this study, we propose the combination of two well-known machine learning (ML) techniques, Bayesian networks (BNs), and association rules (ARs) for modeling the maintenance process by identifying the relationships among the internal and external quality metrics related to a particular project release to both the maintainability of the project and the maintenance process indicators (ie, effort and duration). We also exploit Bayesian inference, to test the effect of certain changes in internal and external project factors to the maintainability of a project. We evaluate our approach through a case study on 957 releases of five open source JavaScript applications. The results show that the maintainability of a release, the changes observed between subsequent releases, and the time required between two releases can be accurately predicted from size, complexity, and activity metrics. The proposed combined approach achieves higher accuracy when evaluated against the BN model accuracy. KEYWORDSdevelopers' activity, JavaScript, maintainability, maintenance, software quality, source code quality 1 | INTRODUCTION Software quality involves many different activities throughout the operational lifecycle of an application all targeted to the delivery of software that meets the specified requirements and satisfies the evolving needs of end-users. Quality concerns are often intertwined with software maintenance that is closely related to technical debt. 1 Software maintenance is one of the most demanding activities during the software lifecycle, consuming up to 75% of the total project resources, 2 mainly due to the many constraints related to the interdependencies of the source code artifacts of the software under maintenance. According to the IEEE 1219 3 software standards document, software maintenance is defined as the "Modification of a software product after delivery to correct faults, to improve performance or other attributes, or to adapt the product to a modified environment." The maintainability is "the ease of performing maintenance activities," while maintenance effort is subsequently defined as the "effort required to reduce or eliminate maintenance problems." Despite the importance of software maintenance and its crucial effect on the quality of a software application over time, its management and assessment is still an immature discipline in software engineering research and practice. The main problem is that software maintainability (as a single synthesized factor) and maintenance process indicators (ie, effort and number of modifications) are most of the times addressed separately without being able to test the effect of the low level internal and external metrics to both maintainability and maintenance indicators at the same time.This gap can be alleviated by modeling the relationship among t...

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“… Discriminative Power is represented as the ability of the independent variable to classify an asset into meaningful groups (as defined by the values of the dependent variables). The values of the dependent variable have been classified into 3 mutually exclusive categories (representing low, medium and high metric values) adopting equal frequency binning [8]. Then Bayesian classifiers [14] are applied in order to derive estimates regarding the discrete values of the dependent variables.…”

Section: E Data Analysismentioning

confidence: 99%

“…In particular, we examined: (a) the reuse process that was introduced by Lambropoulos et al [19], in which a mobile application was developed, based to the best possible extent on open-source software reuse; and (b) replicate the reuse process that was introduced by Ampatzoglou et al [2] for providing an implementation of the Risk game, based on reusable components. The first project was a movie management application (see FIGURE 3) that reused artifacts from eight (8) open-source software systems from the same application domain, whereas the second project was a re-implementation of the famous Risk game that reused artifacts from four (4) open-source software systems.…”

Section: Illustrative Examplementioning

confidence: 99%

Reusability Index: A Measure for Assessing Software Assets Reusability

Ampatzoglou

¹

,

Bibi

²

,

Chatzigeorgiou

³

et al. 2018

New Opportunities for Software Reuse

Self Cite

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To capitalize upon the benefits of software reuse, an efficient selection among candidate reusable assets should be performed in terms of functional fitness and adaptability. The reusability of assets is usually measured through reusability indices. However, these do not capture all facets of reusability, such as structural characteristics, external quality attributes, and documentation. In this paper, we propose a reusability index (REI) as a synthesis of various software metrics and evaluate its ability to quantify reuse, based on IEEE Standard on Software Metrics Validity. The proposed index is compared to existing ones through a case study on 80 reusable open-source assets. To illustrate the applicability of the proposed index we performed a pilot study, where real-world reuse decisions have been compared to decisions imposed by the use of metrics (including REI). The results of the study suggest that the proposed index presents the highest predictive and discriminative power; it is the most consistent in ranking reusable assets, and the most strongly correlated to their levels of reuse. The findings of the paper are discussed to understand the most important aspects in reusability assessment (interpretation of results) and interesting implications for research and practice are provided. 1 Keywords-reuseability; quality model; metrics; validation I. INTRODUCTIONSoftware reuse is considered as a key solution to increase software development productivity and decrease the number of bugs [6], [24]. The process of software reuse entails the use of existing software assets to implement new software systems or extend existing ones [23]. Reuse can take place at multiple levels of granularity, ranging from reusing a few lines of code (usually through the whitebox reuse process) to reusing complete components / libraries (usually as black-box reuse). The process of reusing software assets consists of two major tasks: Reusable Asset Identification. The reuser has to identify an asset (i.e., a piece of source code such as method, class, package or a component / library), which implements the functionality that he/she wants to reuse. This task is challenging since the available amount of assets (either in-house or open source) that can be reused is vast, offering a large variety of candidates. In many cases, the candidate reusable assets are not well organized and documented. The assets that are identified as functionally fitting are then evaluated, based on some criteria, and one is selected for reuse.

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A Bayesian Belief Network for Modeling Open Source Software Maintenance Productivity

Cited by 6 publications

References 22 publications

REI: An integrated measure for software reusability

REI: An integrated measure for software reusability

Maintenance process modeling and dynamic estimations based on Bayesian networks and association rules

Reusability Index: A Measure for Assessing Software Assets Reusability

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