GALE: Geometric Active Learning for Search-Based Software Engineering

Krall, Joseph; Davies, Misty

doi:10.1109/tse.2015.2432024

Cited by 31 publications

(32 citation statements)

References 43 publications

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“…If the accuracy of the model (with more data) does not improve the accuracy when compared to the previous iteration (lesser data), then life is lost. This termination criterion is widely used in the field of Evolutionary Algorithms to determine the degree of convergence [40].…”

Section: Residual-based: "Build An Accurate Model"mentioning

confidence: 99%

Finding Faster Configurations Using FLASH

Nair

Menzies

et al. 2020

IIEEE Trans. Software Eng.

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106

170

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Finding good configurations of a software system is often challenging since the number of configuration options can be large. Software engineers often make poor choices about configuration or, even worse, they usually use a sub-optimal configuration in production, which leads to inadequate performance. To assist engineers in finding the better configuration, this article introduces FLASH, a sequential model-based method that sequentially explores the configuration space by reflecting on the configurations evaluated so far to determine the next best configuration to explore. FLASH scales up to software systems that defeat the prior state-of-the-art model-based methods in this area. FLASH runs much faster than existing methods and can solve both single-objective and multi-objective optimization problems. The central insight of this article is to use the prior knowledge of the configuration space (gained from prior runs) to choose the next promising configuration. This strategy reduces the effort (i.e., number of measurements) required to find the better configuration. We evaluate FLASH using 30 scenarios based on 7 software systems to demonstrate that FLASH saves effort in 100% and 80% of cases in single-objective and multi-objective problems respectively by up to several orders of magnitude compared to state-of-the-art techniques.

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Section: Residual-based: "Build An Accurate Model"mentioning

confidence: 99%

Finding Faster Configurations Using FLASH

Nair

Menzies

et al. 2020

IIEEE Trans. Software Eng.

Self Cite

106

170

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show abstract

“…This paper significantly extends prior work of the authors. In 2014, Krall & Menzies proposed GALE [35]- [37] that solved multiobjective problems via a combination of methods. A subsequent report [64] found that GALE needlessly over-elaborated some aspects of its design.…”

Section: Relation To Prior Workmentioning

confidence: 99%

“…• At the other extreme, the XOMO model discussed below is a much smaller model with continuous-valued decisions and no constraints. • In between these two extremes, we added the POM3 model (that used continuous-valued decisions) since prior work showed that POM3 is very slow to optimize [35]. Another reason to use the models described below is the existence of prior results from these models [35]- [37], [39], [58].…”

Section: Benchmarksmentioning

confidence: 99%

“Sampling” as a Baseline Optimizer for Search-Based Software Engineering

Chen

Nair

Krishna

2019

IIEEE Trans. Software Eng.

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Abstract-Increasingly, Software Engineering (SE) researchers use search-based optimization techniques to solve SE problems with multiple conflicting objectives. These techniques often apply CPU-intensive evolutionary algorithms to explore generations of mutations to a population of candidate solutions. An alternative approach, proposed in this paper, is to start with a very large population and sample down to just the better solutions. We call this method "SWAY ", short for "the sampling way". This paper compares SWAY versus state-of-the-art search-based SE tools using seven models: five software product line models; and two other software process control models (concerned with project management, effort estimation, and selection of requirements) during incremental agile development. For these models, the experiments of this paper show that SWAY is competitive with corresponding state-of-the-art evolutionary algorithms while requiring orders of magnitude fewer evaluations. Considering the simplicity and effectiveness of SWAY , we, therefore, propose this approach as a baseline method for search-based software engineering models, especially for models that are very slow to execute.

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“…8. For the reader unfamiliar with the mutation technique of step 3 and the cdom scoring of step 5, we note that these are standard practice in the search-based SE community [24,25]. • Count how many times n1 that d k is associated with a "best" objective score.…”

Section: A Reporting the Resultsmentioning

confidence: 99%

“SHORT”er Reasoning About Larger Requirements Models

Mathew

Ernst

Klein

2017

2017 IEEE 25th International Requirements Engineering Conference (RE)

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When Requirements Engineering(RE) models are unreasonably complex, they cannot support efficient decision making. SHORT is a tool to simplify that reasoning by exploiting the "key" decisions within RE models. These "keys" have the property that once values are assigned to them, it is very fast to reason over the remaining decisions. Using these "keys", reasoning about RE models can be greatly SHORTened by focusing stakeholder discussion on just these key decisions.This paper evaluates the SHORT tool on eight complex RE models. We find that the number of keys are typically only 12% of all decisions. Since they are so few in number, keys can be used to reason faster about models. For example, using keys, we can optimize over those models (to achieve the most goals at least cost) two to three orders of magnitude faster than standard methods. Better yet, finding those keys is not difficult: SHORT runs in low order polynomial time and terminates in a few minutes for the largest models.

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GALE: Geometric Active Learning for Search-Based Software Engineering

Cited by 31 publications

References 43 publications

Finding Faster Configurations Using FLASH

Finding Faster Configurations Using FLASH

“Sampling” as a Baseline Optimizer for Search-Based Software Engineering

“SHORT”er Reasoning About Larger Requirements Models

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