Solving the Software Project Scheduling Problem with Hyper-heuristics

Andrade, Joaquim de; Silva, Leila; Britto, Alceu S.; Amaral, Rodrigo Reis

doi:10.1007/978-3-030-20912-4_37

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…It has successfully been employed to solve different software engineering problems from a variety of domains. The list includes, but is not limited to, software project scheduling [13], software effort estimation [8], integration and test order problem [29], software product line testing [28], game development [41], failure reproduction [24] and vulnerability testing [23,45].…”

Section: Introductionmentioning

confidence: 99%

Scalability analysis of grammatical evolution based test data generation

Anjum

Ryan

2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

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Heuristic-based search techniques have been increasingly used to automate different aspects of software testing. Several studies suggest that variable interdependencies may exist in branching conditions of real-life programs, and these dependencies result in the need for highly precise data values (such as of the form i=j=k) for code coverage analysis. This requirement makes it very difficult for Genetic Algorithm (GA)-based approach to successfully search for the required test data from vast search spaces of real-life programs.Ariadne is the only Grammatical Evolution (GE)-based test data generation system, proposed to date, that uses grammars to exploit variable interdependencies to improve code coverage. Ariadne has been compared favourably to other well-known test data generation techniques in the literature; however, its scalability has not yet been tested for increasingly complex programs.This paper presents the results of a rigorous analysis performed to examine Ariadne's scalability. We also designed and employed a large set of highly scalable 18 benchmark programs for our experiments. Our results suggest that Ariadne is highly scalable as it exhibited 100% coverage across all the programs of increasing complexity with significantly smaller search costs than GA-based approaches, which failed even with huge search budgets. CCS CONCEPTS• Software and its engineering → Search-based software engineering; Software testing and debugging; • Computing methodologies → Genetic algorithms;

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Section: Introductionmentioning

confidence: 99%

Scalability analysis of grammatical evolution based test data generation

Anjum

Ryan

2020

Proceedings of the 2020 Genetic and Evolutionary Computation Conference

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“…Hyper-heuristics are search methodologies for solving numerous forms of combinatorial optimization problems (COPs) in routing applications [1,2], scheduling [3,4], machine learning [5,6], generation of solvers [7], and software engineering [8][9][10]. They have been applied to several other application domains of combinatorial optimization such as university examination timetabling, university course timetabling, and school timetabling problems [11].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

2022

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Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based iterated local search (EA-ILS) hyper-heuristic that applies a novel mutation operator to control the selection of perturbative low-level heuristics in searching for optimal sequences for performance improvement. The algorithm was compared to existing ones in the hyper-heuristics flexible (HyFlex) framework to demonstrate its performance across the problem domains of knapsack, quadratic assignment, and maximum cut. The comparative results have shown that the EA-ILS hyper-heuristic can obtain the best median objective function values on 22 out of 30 instances in the HyFlex framework. Moreover, it has achieved superiority in its generalization capability when compared to the reported top-performing hyper-heuristic algorithms.

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“…GE is a grammar-based evolutionary algorithm that uses a grammar-based mapping process to separate search space from solution space. In recent years, GE has been successfully adopted to solve many software engineering problems from a wide variety of domains, including software effort estimation [11], vulnerability testing [55], integration and test order problem [39], game development [51], failure reproduction [35], software project scheduling [19] and software product line testing [37]. To the best of our knowledge, Ariadne is the only GE-based system proposed to date that targets the structural coverage testing of procedural C/C++ programs.…”

Section: Introductionmentioning

confidence: 99%

Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing

Anjum

Ryan

2021

SN COMPUT. SCI.

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Heuristic-based optimization techniques have been increasingly used to automate different types of code coverage analysis. Several studies suggest that interdependencies (in the form of comparisons) may exist between the condition constructs, of variables and constant values, in the branching conditions of real-world programs, e.g. ($$i \le 100$$ i ≤ 100 ) or ($$i==j$$ i = = j ), etc. In this work, by interdependencies we refer to the situations where, to satisfy a branching condition, there must be a certain relationship between the values of some specific condition constructs (which may or may not be a part of the respective condition predicates). For example, the values of variables i and j must be equal to satisfy the condition of ($$i==j$$ i = = j ), and the value of variable k must be equal to 100 for the satisfaction of the condition of ($$k==100$$ k = = 100 ). To date, only the Ariadne, a Grammatical Evolution (GE)-based system, exploits these interdependencies between input variables (e.g. of the form ($$i \le j$$ i ≤ j ) or ($$i==j$$ i = = j ), etc.) to efficiently generate test data. Ariadne employs a simple attribute grammar to exploit these dependencies, which enables it to evolve complex test data, and has been compared favourably to other well-known techniques in the literature. However, Ariadne does not benefit from interdependencies involving constants, e.g. ($$i \le 100$$ i ≤ 100 ) or ($$j==500$$ j = = 500 ), etc., due to the difficulty in evolving precise values, and these are equally important constructs of condition predicates. Furthermore, constant creation in GE can be difficult, particularly with high precision. We propose to seed the grammar with constants extracted from the source code of the program under test to enhance and extend Ariadne’s capability to exploit richer types of dependencies (involving all combinations of both variables and constant values). We compared our results with the original system of Ariadne against a large set of benchmark problems which include 10 numeric programs in addition to the ones originally used for Ariadne. Our results demonstrate that the seeding strategy not only dramatically improves the generality of the system, as it improves the code coverage (effectiveness) by impressive margins, but it also reduces the search budgets (efficiency) often up to an order of magnitude. Moreover, we also performed a rigorous analysis to investigate the scalability of our improved Ariadne, showing that it stays highly scalable when compared to both the original system of Ariadne and GA-based test data generation approach.

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Solving the Software Project Scheduling Problem with Hyper-heuristics

Cited by 8 publications

References 16 publications

Scalability analysis of grammatical evolution based test data generation

Scalability analysis of grammatical evolution based test data generation

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

Seeding Grammars in Grammatical Evolution to Improve Search-Based Software Testing

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