Assessing the Bug-Prediction with Re-Usability Based Package Organization for Object Oriented Software Systems

Shaikh, Mohsin; Lee, Ki-Seong; Lee, Chan-Gun

doi:10.1587/transinf.2016edp7186

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…All these efforts form the motivation of our study to further investigate dependency based structural properties of packages. In particular, study extends the evidence for role f package based design in improvement of fault-prediction and effectiveness in describing software quality attributes [36], [37].…”

Section: Related Worksupporting

confidence: 56%

Evaluating Dependency based Package-level Metrics for Multi-objective Maintenance Tasks

Shaikh¹,

Jalbani²,

Ansari³

et al. 2017

ijacsa

Self Cite

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Abstract-Role of packages in organization and maintenance of software systems has acquired vital importance in recent research of software quality. With an advancement in modularization approaches of object oriented software, packages are widely considered as re-usable and maintainable entities of objectoriented software architectures, specially to avoid complicated dependencies and insure software design of well identified services. In this context, recently research study of H. Abdeen on automatic optimization of package dependencies provide composite frame of metrics for package quality and overall source code modularization. There is an opportunity to conduct comprehensive empirical analysis over proposed metrics for assessing their usefulness and application for fault-prediction, design flaw detection, identification of source code anomalies and architectural erosion. In this paper, we examine impact of these dependency optimization based metrics in wide spectrum of software quality for single package and entire software modularization. Our experimental work is conducted over open source software systems through statistical methodology based on cross validation fault-prediction and correlation. We conclude with empirical evidence that dependency based package modularization metrics provide more accurate view for predicting fault-prone packages and improvement of overall software structure. Thus, application of these metrics can help the developers and software practitioners to insure proactive management of the source code dependencies and avoid design flaws during software development.

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Section: Related Worksupporting

confidence: 56%

Evaluating Dependency based Package-level Metrics for Multi-objective Maintenance Tasks

Shaikh¹,

Jalbani²,

Ansari³

et al. 2017

ijacsa

Self Cite

View full text Add to dashboard Cite

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Effort-Aware Fault-Proneness Prediction Using Non-API-Based Package-Modularization Metrics

Shaikh,

Tunio,

Khan

et al. 2024

Mathematics

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Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary aspects of legacy OO systems through package-modularization metrics. These package-modularization metrics basically address non-API-based object-oriented principles, like encapsulation, commonality-of-goal, changeability, maintainability, and analyzability. Despite their ability to characterize package organization, their application towards cost-effective fault-proneness prediction is yet to be determined. In this paper, we present theoretical illustration and empirical perspective of non-API-based package-modularization metrics towards effort-aware fault-proneness prediction. First, we employ correlation analysis to evaluate the relationship between faults and package-level metrics. Second, we use multivariate logistic regression with effort-aware performance indicators (ranking and classification) to investigate the practical application of proposed metrics. Our experimental analysis over open-source Java software systems provides statistical evidence for fault-proneness prediction and relatively better explanatory power than traditional metrics. Consequently, these results guide developers for reliable and modular package-based software design.

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DIAFM: An Improved and Novel Approach for Incremental Frequent Itemset Mining

Shaikh,

Akram,

Khan

et al. 2024

Mathematics

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Traditional approaches to data mining are generally designed for small, centralized, and static datasets. However, when a dataset grows at an enormous rate, the algorithms become infeasible in terms of huge consumption of computational and I/O resources. Frequent itemset mining (FIM) is one of the key algorithms in data mining and finds applications in a variety of domains; however, traditional algorithms do face problems in efficiently processing large and dynamic datasets. This research introduces a distributed incremental approximation frequent itemset mining (DIAFM) algorithm that tackles the mentioned challenges using shard-based approximation within the MapReduce framework. DIAFM minimizes the computational overhead of a program by reducing dataset scans, bypassing exact support checks, and incorporating shard-level error thresholds for an appropriate trade-off between efficiency and accuracy. Extensive experiments have demonstrated that DIAFM reduces runtime by 40–60% compared to traditional methods with losses in accuracy within 1–5%, even for datasets over 500,000 transactions. Its incremental nature ensures that new data increments are handled efficiently without needing to reprocess the entire dataset, making it particularly suitable for real-time, large-scale applications such as transaction analysis and IoT data streams. These results demonstrate the scalability, robustness, and practical applicability of DIAFM and establish it as a competitive and efficient solution for mining frequent itemsets in distributed, dynamic environments.

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Assessing the Bug-Prediction with Re-Usability Based Package Organization for Object Oriented Software Systems

Cited by 4 publications

References 30 publications

Evaluating Dependency based Package-level Metrics for Multi-objective Maintenance Tasks

Evaluating Dependency based Package-level Metrics for Multi-objective Maintenance Tasks

Effort-Aware Fault-Proneness Prediction Using Non-API-Based Package-Modularization Metrics

DIAFM: An Improved and Novel Approach for Incremental Frequent Itemset Mining

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