Interactive multi-objective evolutionary optimization of software architectures

Ramírez, Aurora; Romero, José Raúl; Ventura, Sebastián

doi:10.1016/j.ins.2018.06.034

Cited by 26 publications

(5 citation statements)

References 31 publications

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“…Figure 7 shows the time in seconds dedicated (on average) by participants per interaction. The time clearly decreases as the execution progresses, which is a common phenomenon in iSBSE experiments [26]. The main causes are the inherent fatigue and the repetitive nature of the process, leading humans to pay more attention at the beginning.…”

Section: Rq3: Interaction Processmentioning

confidence: 99%

“…Many software engineering tasks reformulated as optimization problems can benefit from the interaction with the engineer. Modeling and design involve complex cognitive tasks in which the engineer's subjective judgment becomes relevant to evaluate the quality of the candidates [18], [26]. However, the use of interactive approaches has been broadened to other tasks that have been traditionally solved in a fully automated way, such as refactoring [19], [27] and testing [20], [21], [28].…”

Section: Interactivity In Sbstmentioning

confidence: 99%

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InterEvo-TR: Interactive Evolutionary Test Generation With Readability Assessment

Delgado‐Pérez¹,

Ramírez²,

Valle-Gomez³

et al. 2023

IIEEE Trans. Software Eng.

Self Cite

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Automated test case generation has proven to be useful to reduce the usually high expenses of software testing. However, several studies have also noted the skepticism of testers regarding the comprehension of generated test suites when compared to manually designed ones. This fact suggests that involving testers in the test generation process could be helpful to increase their acceptance of automatically-produced test suites. In this paper, we propose incorporating interactive readability assessments made by a tester into EvoSuite, a widely-known evolutionary test generation tool. Our approach, INTEREVO-TR, interacts with the tester at different moments during the search and shows different test cases covering the same coverage target for their subjective evaluation. The design of such an interactive approach involves a schedule of interaction, a method to diversify the selected targets, a plan to save and handle the readability values, and some mechanisms to customize the level of engagement in the revision, among other aspects. To analyze the potential and practicability of our proposal, we conduct a controlled experiment in which 39 participants, including academics, professional developers, and student collaborators, interact with INTEREVO-TR. Our results show that the strategy to select and present intermediate results is effective for the purpose of readability assessment. Furthermore, the participants' actions and responses to a questionnaire allowed us to analyze the aspects influencing test code readability and the benefits and limitations of an interactive approach in the context of test case generation, paving the way for future developments based on interactivity.

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Section: Rq3: Interaction Processmentioning

confidence: 99%

Section: Interactivity In Sbstmentioning

confidence: 99%

InterEvo-TR: Interactive Evolutionary Test Generation With Readability Assessment

Delgado‐Pérez¹,

Ramírez²,

Valle-Gomez³

et al. 2023

IIEEE Trans. Software Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, it is proven that an automated search-based refactoring technique does not necessarily lead to an improved version of the program. 10,71,72 Including developers in the refactoring process, as suggested, 20,72,73 may be a way to refactor software to be more helpful to the developers.…”

Section: Interactive Software Refactoringmentioning

confidence: 99%

Model‐based source code refactoring with interaction and visual cues

Hemati Moghadam,

Ó Cinnéide,

Sardarian

et al. 2023

J Software Evolu Process

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Refactoring source code involves the developer in a myriad of program detail that can obscure the design changes that they actually wish to bring about. On the other hand, refactoring a UML model of the code makes it easier to focus on the program design, but the burdensome task of applying the refactorings to the source code is left to the developer. In an attempt to obtain the advantages of both approaches, we propose a refactoring approach where the interaction with the developer takes place at the model level, but the actual refactoring occurs on the source code itself. We call this approach model‐based source code refactoring and implement it in this paper using two tools: (1) Design‐Imp enables the developer to use interactive search‐based design exploration to create a UML‐based desired design from an initial design extracted from the source code. It also provides visual cues to improve developer comprehension during the design‐level refactoring process and to help the developer to discern between promising and poor refactoring solutions. (2) Code‐Imp then refactors the original source so that it has the same functional behavior as the original program, and a design close to the one produced in the design exploration phase, that is, a design that has been confirmed as “desirable” by the developer. We evaluated our approach involving interaction and visual cues with industrial developers refactoring three Java projects, comparing it with an approach using interaction without visual cues and a fully automated approach. The results show that our approach yields refactoring sequences that are more acceptable both to the individual developer and to a set of independent expert refactoring evaluators. Furthermore, our approach removed more code smells and was evaluated very positively by the experiment participants.

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“…Gu et al [32] incorporated Neural Network-based learning technique, General Regression Neural Network (GRNN), into IEC to approximate the user's aesthetic perception during the interactive evolutionary process. IGA-based systems have also been proposed for aircraft [33], software design [34] and structural design [35].…”

Section: Interactive Designmentioning

confidence: 99%

GenYacht: An interactive generative design system for computer-aided yacht hull design

2019

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In the present work, a new digital design system, GenYacht, is proposed for the creation of optimal and user-centred yacht hull forms. GenYacht is a hybrid system involving generative and interactive design approaches, which enables users to create a variety of design alternatives. Among them, a user can select a hull design with desirable characteristics based on its appearance and hydrostatics/hydrodynamic performance. GenYacht first explores a given design space using a generative design technique (GDT), which creates uniformly distributed designs satisfying the given design constraints. These designs are then presented to a user and single or multiple designs are selected based on the user's requirements. Afterwards, based on the selections, the design space is refined using a novel space-shrinking technique (SST). In each interaction, SST shrinks the design space, which is then fed into GDT to create new designs in the shrank space for the next interaction. This shrinkage of design space guides the exploration process and focuses the computational efforts on user-preferred regions. The interactive and generative design steps are repeated until the user reaches a satisfactory design(s). The efficiency of GenYacht is demonstrated via experimental and user studies and its performance is compared with interactive genetic algorithms.

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Interactive multi-objective evolutionary optimization of software architectures

Cited by 26 publications

References 31 publications

InterEvo-TR: Interactive Evolutionary Test Generation With Readability Assessment

InterEvo-TR: Interactive Evolutionary Test Generation With Readability Assessment

Model‐based source code refactoring with interaction and visual cues

GenYacht: An interactive generative design system for computer-aided yacht hull design

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