Titlebook: Search Based Software Engineering; 5th International Sy Günther Ruhe,Yuanyuan Zhang Conference proceedings 2013 Springer-Verlag Berlin Heid

Invigorate

Predicting Regression Test Failures Using Genetic Algorithm-Selected Dynamic Performance Analysis Meols to capture the runtime behaviour of a program as it executes each test in a regression suite. The performance information is then used to build a dynamically predictive model of test outcomes. Our framework is evaluated using a genetic algorithm for dynamic metric selection in combination with s

Asperity

A Recoverable Robust Approach for the Next Release Problem issue in the iterative and incremental software development model. Since software development is performed under a dynamic environment, some requirements aspects, like importance and effort cost values, are highly subject to uncertainties, which should be taken into account when solving this proble

CAJ

A Systematic Review of Software Requirements Selection and Prioritization Using SBSE Approaches finding and selecting a set of requirements that may be part of a software release. This paper uses a systematic review to investigate which SBSE approaches have been proposed to address software requirement selection and prioritization problems. The search strategy identified 30 articles in this a

胡言乱语

PATRI

寡头政治

On the Synergy between Search-Based and Search-Driven Software Engineeringotivated optimization algorithms, and the notion of searching for reusable components using query-driven search engines. To date these possibilities have largely been explored separately within different communities. In this paper we suggest there is a synergy between the two approaches, and that a

LAPSE

Preference-Based Many-Objective Evolutionary Testing Generates Harder Test Cases for Autonomous Agen testing. The most prominent work in this direction is by Nguyen et al. [13], which formulates this problem as a bi-objective optimization problem to search for hard test cases from a robustness viewpoint. In this paper, we extend this work by: (1) proposing a new seven-objective formulation of this

协迫

冒失

Predicting Regression Test Failures Using Genetic Algorithm-Selected Dynamic Performance Analysis Metate-of-the-art machine learning classifiers. We show that if a program is modified and some tests subsequently fail, then it is possible to predict with considerable accuracy which of the remaining tests will also fail which can be used to help prioritise tests in time constrained testing environments.

六边形