Learning Software Configuration Spaces: A Systematic Literature Review

Pereira, Juliana Alves; Martin, Hugo; Acher, Mathieu; Jézéquel, Jean-Marc; Botterweck, Goetz; Ventresque, Anthony

doi:10.48550/arxiv.1906.03018

Cited by 10 publications

(12 citation statements)

References 97 publications

(521 reference statements)

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“…• TL methods have different time and space complexities and energy consumption [117], which have to be measured when designing modeling approaches to decide if the gain in performance is worth it.…”

Section: Modeling Efficiency and Tl Methodsmentioning

confidence: 99%

Transfer Learning Based Performance Modeling And Effective Storage Management In Big Data Ecosystems

Alsayoud¹

2021

Preprint

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Big data ecosystems contain a mix of sophisticated hardware storage components to support heterogeneous workloads. Storage components and the workloads interact and affect each other; therefore, their relationship has to consider when modeling workloads or managing storage. Efficient workload modeling guides optimal storage management decisions, and the right decisions help guarantee the workload’s needs. The first part of this thesis focuses on workload modeling efficiency, and the second part focuses on cost-effective storage management.<div>Workload performance modeling is an essential step in management decisions. The standard modeling approach constructs the model based on a historical dataset collected from one set of setups (scenario). The standard modeling approach requires the model to be reconstructed from scratch with every time the setups changes. To address this issue, we propose a cross-scenario modeling approach that improves the workload’s performance classification accuracy by up to 78% through adopting the Transfer Learning (TL).<br></div><div>The storage system is the most crucial component of the big data ecosystem, where the workload’s execution process starts by fetching data from it and ends by storing data into it. Thus, the workload’s performance is directly affected by storage capability. To provide a high I/O performance in the ecosystems, Solid State Drive (SSD) are utilized as a tier or as a cache on big data distributed ecosystems. SSDs have a short lifespan that is affected by data size and the number of writing operations. Balancing performance requirements and SSD’s lifespan consumption is never easy, and it’s even harder when interacting with a huge amount of data and with heterogeneous I/O patterns. In this thesis, we analysis big data workloads I/O pattern impacts on SSD’s lifespan when SSD is used as a tier or as a cache. Then, we design a Hidden Markov Model (HMM) based I/O pattern controller that manages workload placement and guarantees cost-effective storage that enhances the workload performance by up to 60%, and improves SSD’s lifespan by up to 40%. </div><div>The designed transfer learning modeling approach and the storage management solutions improve workload modeling accuracy, and the quality of the storage management policies while the testing setup changes.<br></div>

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Section: Modeling Efficiency and Tl Methodsmentioning

confidence: 99%

Transfer Learning Based Performance Modeling And Effective Storage Management In Big Data Ecosystems

Alsayoud¹

2021

Preprint

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“…only a small number of configurations and their interactions have a significant impact on system performance [14]. Various tools and techniques are being explored to limit such configuration spaces [17,27]. Most literature agree that domain expertise is often the best and fastest way to eliminate unwanted features (configuration objects in the problem) [17].…”

Section: Identifying Unimportant Cvsmentioning

confidence: 99%

Performance Health Index for Complex Cyber Infrastructures

Sondur¹,

Kant²

2021

Preprint

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Most IT systems depend on a set of configuration variables (CVs), expressed as a name/value pair that collectively define the resource allocation for the system. While the ill-effects of misconfiguration or improper resource allocation are well-known, there is no effective a priori metric to quantify the impact of the configuration on the desired system attributes such as performance, availability, etc. In this paper, we propose a Configuration Health Index (CHI) framework specifically attuned to the performance attribute to capture the influence of CVs on the performance aspects of the system. We show how CHI, which is defined as a configuration scoring system, can take advantage of the domain knowledge and the available (but rather limited) performance data to produce important insights into the configuration settings. We compare the CHI with both well-advertised segmented non-linear models and state-of-the-art data-driven models, and show that the CHI not only consistently provides better results but also avoids the dangers of pure data drive approach which may predict incorrect behavior or eliminate some essential configuration variables from consideration.

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“…Defect prediction presents a natural application avenue for machine learning in product line engineering (a paradigm for engineering variantrich software systems [48]). Most existing work in this area has focused on the sampling of configurations for various use cases; the recent survey by Pereira et al [49] provides an overview. Focusing on performance predictions, Siegmund et al [50] use machine learning and sampling techniques to build performance influence models, quantifying the performance impact of specific features and interactions.…”

Section: Background and Related Workmentioning

confidence: 99%

Feature-Oriented Defect Prediction: Scenarios, Metrics, and Classifiers

Mukelabai,

Strüder,

Strüber

et al. 2021

Preprint

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Software defects are a major nuisance in software development and can lead to considerable financial losses or reputation damage for companies. To this end, a large number of techniques for predicting software defects, largely based on machine learning methods, has been developed over the past decades. These techniques usually rely on code-structure and process metrics to predict defects at the granularity of typical software assets, such as subsystems, components, and files. In this paper, we systematically investigate feature-oriented defect prediction: predicting defects at the granularity of features-domain-entities that abstractly represent software functionality and often cross-cut software assets. Feature-oriented prediction can be beneficial, since: (i) particular features might be more error-prone than others, (ii) characteristics of features known as defective might be useful to predict other error-prone features, and (iii) feature-specific code might be especially prone to faults arising from feature interactions. We explore the feasibility and solution space for feature-oriented defect prediction. We design and investigate scenarios, metrics, and classifiers. Our study relies on 12 software projects from which we analyzed 13,685 bug-introducing and corrective commits, and systematically generated 62,868 training and test datasets to evaluate the designed classifiers, metrics, and scenarios. The datasets were generated based on the 13,685 commits, 81 releases, and 24, 532 permutations of our 12 projects depending on the scenario addressed. We covered scenarios, such as just-in-time (JIT) and cross-project defect prediction. Our results confirm the feasibility of feature-oriented defect prediction. We found the best performance (i.e., precision and robustness) when using the Random Forest classifier, with process and structure metrics. Surprisingly, we found high performance for single-project JIT (median AUROC ≥ 95 %) and release-level (median AUROC ≥ 90 %) defect prediction-contrary to studies that assert poor performance due to insufficient training data. Lastly, we found that a model trained on release-level data from one of the twelve projects could predict defect-proneness of features in the other eleven projects with median performance of 82 %, without retraining on the target projects. Our results suggest potential for defect-prediction model-reuse across projects, as well as more reliable defect predictions for developers as they modify or release software features.

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Learning Software Configuration Spaces: A Systematic Literature Review

Cited by 10 publications

References 97 publications

Transfer Learning Based Performance Modeling And Effective Storage Management In Big Data Ecosystems

Transfer Learning Based Performance Modeling And Effective Storage Management In Big Data Ecosystems

Performance Health Index for Complex Cyber Infrastructures

Feature-Oriented Defect Prediction: Scenarios, Metrics, and Classifiers

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