Machine Learning for User Modeling in an Interactive Genetic Algorithm for the Next Release Problem

Araújo, Allysson Allex; Paixao, Matheus

doi:10.1007/978-3-319-09940-8_17

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…As shown in Table 4, we use two or three common objectives for all software projects/versions, namely penalty score, cost, and coverage, which have the objective functions as below [3,35,60,105,106]:…”

Section: Objectivementioning

confidence: 99%

The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software Engineering

Chen¹,

Li²

2023

ACM Trans. Softw. Eng. Methodol.

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In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem’s Pareto optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights which reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice since it simplifies the search via converting the original multi-objective problem into a single-objective one and enable the search to focus on what only the stakeholders are interested in. This paper questions such a “weighted search first” belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large scale empirical study which consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is at the majority of the time (up to 77% of the cases) significantly better than its weighted counterpart, as long as we allow a sufficient, but not unrealistic search budget. This is a beneficial result, as it discovers a potentially new “rule-of-thumb” for the SBSE community: even when clear preferences are available, it is recommended to always consider Pareto search by default for multi-objective SBSE problems provided that solution quality is more important. Weighted search, in contrast, should only be preferred when the resource/search budget is limited, especially for expensive SBSE problems. This, together with other findings and actionable suggestions in the paper, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at: https://github.com/ideas-labo/pareto-vs-weight-for-sbse.

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Section: Objectivementioning

confidence: 99%

The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software Engineering

Chen¹,

Li²

2023

ACM Trans. Softw. Eng. Methodol.

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“…Fuchshuber et al [15] modify the hill climbing algorithm with some patterns observed from the terrain visualization. Araújo et al [16] draw machine learning models into the NRP. Harman et al [17] analyze the NRP from the perspective of requirement sensitivity analysis.…”

Section: Algorithm 2: Search Space Smoothingmentioning

confidence: 99%

Transformed Search Based Software Engineering: A New Paradigm of SBSE

Jiang

Zhang

et al. 2015

Search-Based Software Engineering

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Abstract. Recent years have witnessed the sharp growth of research interests in Search Based Software Engineering (SBSE) from the society of Software Engineering (SE). In SBSE, a SE task is generally transferred into a combinatorial optimization problem and search algorithms are employed to achieve solutions within its search space. Since the terrain of the search space is rugged with numerous local optima, it remains a great challenge for search algorithms to achieve high-quality solutions in SBSE. In this paper, we propose a new paradigm of SBSE, namely Transformed Search Based Software Engineering (TSBSE). Given a new SE task, TSBSE first transforms its search space into either a reduced one or a series of gradually smoothed spaces, then employ search algorithms to effectively seek high-quality solutions. More specifically, we investigate two techniques for TSBSE, namely search space reduction and search space smoothing. We demonstrate the effectiveness of these new techniques over a typical SE task, namely the Next Release Problem (NRP). The work of this paper provides a new way for tackling SE tasks in SBSE.

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“…Finally, the threats to the validity of the empirical study are discussed. For the Automatic Validation, a simulator was developed to represent a possible human evaluation profile, which was a similar approach to [41] and [16].…”

Section: Validationmentioning

confidence: 99%

“…Araújo and Paixão [16] proposed an architecture to allows the decision maker 30 to take part in a search-based approach to the NRP. It was employed an Interactive Genetic Algorithm (IGA) and the approach consists of requiring subjective evaluations from the user to each individual during the algorithm evolution, while a Learning Model is used to learn the human behavior and, eventually, replace the decision maker in the remainder of the process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Incorporating user preferences in ant colony optimization for the next release problem

Ferreira

Arajo

Neto

et al. 2016

Applied Soft Computing

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Machine Learning for User Modeling in an Interactive Genetic Algorithm for the Next Release Problem

Cited by 9 publications

References 7 publications

The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software Engineering

The Weights Can Be Harmful: Pareto Search versus Weighted Search in Multi-objective Search-based Software Engineering

Transformed Search Based Software Engineering: A New Paradigm of SBSE

Incorporating user preferences in ant colony optimization for the next release problem

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