Evolutionary software engineering, a review

Mantere, Timo; Alander, Jarmo T.

doi:10.1016/j.asoc.2004.08.004

Cited by 80 publications

(48 citation statements)

References 23 publications

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“…As a consequence, they have attracted growing interest from many researchers in recent years. On the other hand, the nature of Software Engineering problems is ideal for the application of metaheuristic techniques, as is shown in the work of Harman & Jones [27], and besides they obtain good results in test case generations [38]. The search space of solutions in test case generation is very large and many metaheuristic techniques explore a region closer to a specific solution.…”

Section: Introductionmentioning

confidence: 99%

Automated test data generation using a scatter search approach

Blanco

Tuya

Adenso‐Díaz

2009

Information and Software Technology

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The techniques for the automatic generation of test cases try to efficiently find a small set of cases that allow a given adequacy criterion to be fulfilled, thus contributing to a reduction in the cost of software testing. In this paper we present and analyze two versions of an approach based on the Scatter Search metaheuristic technique for the automatic generation of software test cases using a branch coverage adequacy criterion. The first test case generator, called TCSS, uses a diversity property to extend the search of test cases to all branches of the program under test in order to generate test cases that cover these. The second, called TCSS-LS, is an extension of the previous test case generator which combines the diversity property with a local search method that allows the intensification of the search for test cases that cover the difficult branches. We present the results obtained by our generators and carry out a detailed comparison with many other generators, showing a good performance of our approach.

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Section: Introductionmentioning

confidence: 99%

Automated test data generation using a scatter search approach

Blanco

Tuya

Adenso‐Díaz

2009

Information and Software Technology

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“…Evolutionary algorithms often perform well in all types of problems because they typically do not make any assumption about the underlying fitness landscape; this generality has led to success in broad fields such as engineering [31,28], biology [21], economics [12], physics [2], medicine [43], ecology [33], information retrieval [13], etc.…”

Section: Evolutionary Algorithmmentioning

confidence: 99%

Evolutionary discriminative confidence estimation for spoken term detection

Tejedor

Echeverría

Wang

et al. 2011

Multimed Tools Appl

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract Spoken term detection (STD) is the task of searching for occurrences of spoken terms in audio archives. It relies on robust confidence estimation to make a hit/false alarm (FA) decision. In order to optimize the decision in terms of the STD evaluation metric, the confidence has to be discriminative. Multi-layer perceptrons (MLPs) and support vector machines (SVMs) exhibit good performance in producing discriminative confidence; however they are severely limited by the continuous objective functions, and are therefore less capable of dealing with complex decision tasks. This leads to a substantial performance reduction when measuring detection of out-of-vocabulary (OOV) terms, where the high diversity in term properties usually leads to a complicated decision boundary. In this paper we present a new discriminative confidence estimation approach based on evolutionary discriminant analysis (EDA). Unlike MLPs and SVMs, EDA uses the classification error as its objective function, resulting in a model optimized towards the evaluation metric. In addition, EDA combines heterogeneous projection functions and classification strategies in decision making, leading to a highly flexible classifier that is capable of dealing with complex decision tasks. Finally, the evolutionary strategy of EDA re- duces the risk of local minima. We tested the EDA-based confidence with a state-of-the-art phoneme-based STD system on an English meeting domain corpus, which employs a phoneme speech recognition system to produce lattices within which the phoneme sequences corresponding to the enquiry terms are searched. The test corpora comprise 11 hours of speech data recorded with individual head-mounted microphones from 30 meetings carried out at several institutes including ICSI; NIST; ISL; LDC; the Virginia Polytechnic Institute and State University; and the University of Edinburgh. The experimental results demonstrate that EDA considerably outperforms MLPs and SVMs on both classification and confidence measurement in STD, and the advantage is found to be more significant on OOV terms than on in-vocabulary (INV) terms. In terms of classification performance, EDA achieved an equal error rate (EER) of 11% on OOV terms, compared to 34% and 31% with MLPs and SVMs respectively; for INV terms, an EER of 15% was obtained with EDA compared to 17% obtained with MLPs and SVMs. In terms of STD performance for OOV terms, EDA presented a significant relative improvement of 1.4% and 2.5% in terms of average term-weighted value (ATWV) over MLPs and SVMs respectively.

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“…Mantere and Alander in [35] present a recent review on the application of evolutionary algorithms to software testing. Most of the papers included in their discussion use genetic algorithms (GAs) to find test data.…”

Section: Introductionmentioning

confidence: 99%

Observations in using parallel and sequential evolutionary algorithms for automatic software testing

Alba

Chicano

2008

Computers & Operations Research

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In this paper we analyze the application of parallel and sequential evolutionary algorithms to the automatic test data generation problem. The problem consists of automatically creating a set of input data to test a program. This is a fundamental step in software development and a time consuming task in existing software companies. Canonical sequential evolutionary algorithms have been used in the past for this task. We explore here the use of parallel evolutionary algorithms. Evidence of greater efficiency, larger diversity maintenance, additional availability of memory/CPU, and multi-solution capabilities of the parallel approach, reinforce the importance of the advances in research with these algorithms. We describe in this work how canonical genetic algorithms (GAs) and evolutionary strategies (ESs) can help in software testing, and what the advantages are (if any) of using decentralized populations in these techniques. In addition, we study the influence of some parameters of the proposed test data generator in the results. For the experiments we use a large benchmark composed of twelve programs that includes fundamental algorithms in computer science.

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Evolutionary software engineering, a review

Cited by 80 publications

References 23 publications

Automated test data generation using a scatter search approach

Automated test data generation using a scatter search approach

Evolutionary discriminative confidence estimation for spoken term detection

Observations in using parallel and sequential evolutionary algorithms for automatic software testing

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