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Influence of the 1990 IEEE TSE Paper "Automated software Test Data Generation" on software engineering

IEEE TRANSACTIONS ON software engineering 2025年第3期51卷 751-753页

作者： Korel, Bogdan IIT Dept Comp Sci Chicago IL 60616 USA

Generating test data is a critical component of software testing, essential for ensuring the proper functioning of software systems. Automated test data generation, in particular, can greatly improve the efficiency and accuracy of the testing process. In 1990, the paper "Automated software Test Data Generation," published in IEEE Transactions on software engineering (Korel, 1990), introduced a novel approach to automated test data generation, focusing on actual program execution, fitness function minimization methods, and dynamic data flow analysis. This paper discusses the impact of the 1990 IEEE TSE publication (Korel, 1990) on automated test data generation and software engineering.

关键词： software Data collection software engineering software reliability Reliability software testing software algorithms software systems Genetics Computer science Automated test data generation search-based test generation fitness function dynamic data flow analysis alternating variable method search-based software engineering

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Guest editorial: search-based software engineering

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Empirical software engineering 2014年 1-3页

作者： Gordon Fraser Jerffeson Teixeira de Souza Department of Computer Science University of Sheffield Sheffield UK Optimization in Software Engineering Group State University of Ceará Fortaleza Brazil

search-based software engineering (SBSE) models complex software engineering problems as optimization problems, and uses metaheuristic search algorithms to solve these. SBSE has the potential to address the challenges posed by the growing size of complexity of modern computer systems, and has already been successfully applied to solve problems in nearly all software development life cycle phases. This special issue presents three articles demonstrating the versatility of SBSE by tackling three very different aspects of software engineering: software module clustering, network optimisation, requirements for dynamic adaptive systems.

关键词： search-based software engineering

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Parameter tuning or default values? An empirical investigation in search-based software engineering

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EMPIRICAL software engineering 2013年第3期18卷 594-623页

作者： Arcuri, Andrea Fraser, Gordon Simula Res Lab Certus Software V&V Ctr Lysaker Norway Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

Many software engineering problems have been addressed with search algorithms. search algorithms usually depend on several parameters (e.g., population size and crossover rate in genetic algorithms), and the choice of these parameters can have an impact on the performance of the algorithm. It has been formally proven in the No Free Lunch theorem that it is impossible to tune a search algorithm such that it will have optimal settings for all possible problems. So, how to properly set the parameters of a search algorithm for a given software engineering problem? In this paper, we carry out the largest empirical analysis so far on parameter tuning in search-based software engineering. More than one million experiments were carried out and statistically analyzed in the context of test data generation for object-oriented software using the EvoSuite tool. Results show that tuning does indeed have impact on the performance of a search algorithm. But, at least in the context of test data generation, it does not seem easy to find good settings that significantly outperform the "default" values suggested in the literature. This has very practical value for both researchers (e.g., when different techniques are compared) and practitioners. Using "default" values is a reasonable and justified choice, whereas parameter tuning is a long and expensive process that might or might not pay off in the end.

关键词： search-based software engineering Test data generation Object-oriented Unit testing Tuning EvoSuite Java Response surface Design of experiments

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Boosting bug localization in software models of video games with simulations and component-specific genetic operations

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software AND SYSTEMS MODELING 2025年 1-29页

作者： Casamayor, Rodrigo Arcega, Lorena Perez, Francisca Cetina, Carlos Univ San Jorge SVIT Res Grp Zaragoza Spain Univ Politecn Valencia PROS Res Ctr VRAIN Valencia Spain

The development and maintenance of video games present unique challenges that differentiate them from Classic software engineering (CSE) such as the increased difficulty in locating bugs within video games. This distinction has given rise to Game software engineering (GSE), a subfield that intersects software engineering and video games. Our work proposes a novel way for bug localization in video games by evolving simulations via an evolutionary algorithm, which helps to explore the large number of possible simulations. Simulations generate data (i.e., traces) from the behavior of non-player characters (NPCs). NPCs are not controlled by the player and are key components of video games. We hypothesize that such traces can be instrumental in locating bugs. Our approach automatically locates potential buggy model elements from traces. Furthermore, we propose a novel way of applying genetic operations to evolve simulations by selectively combining their components, rather than combining all components as a whole. We evaluate our approach in the commercial video game Kromaia, and the results indicate that evolving simulations using our novel component-specific genetic operations boosts bug localization. Specifically, our approach improved the F-measure for all bug categories over randomly combining all components, the baseline (which focuses on CSE and utilizes bug reports), and Random search by 7.93%, 27.17%, and 46.34%, respectively. This work opens a new research direction for further exploration in bug localization within GSE and potentially in CSE as well. Moreover, it encourages other researchers to explore alternative genetic operations rather than selecting them by default.

关键词： Bug localization Video games search-based software engineering Model-driven engineering

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Pareto-Optimal search-based software engineering (POSBSE): A Literature Survey

Pareto-Optimal Search-Based Software Engineering (POSBSE): A...

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2nd International Workshop on Realizing Artificial Intelligence Synergies in software engineering (RAISE)

作者： Sayyad, Abdel Salam Ammar, Hany W Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781467364379

The search-based software engineering (SBSE) community is increasingly recognizing the inherit "multiobjectiveness" in software engineering problems. The old ways of aggregating all objectives into one may very well be behind us. We perform a well-deserved literature survey of SBSE papers that used multiobjective search to find Pareto-optimal solutions, and we pay special attention to the chosen algorithms, tools, and quality indicators, if any. We conclude that the SBSE field has seen a trend of adopting the Multiobjective Evolutionary Optimization Algorithms (MEOAs) that are widely used in other fields (such as NSGA-II and SPEA2) without much scrutiny into the reason why one algorithm should be preferred over the others. We also find that the majority of published work only tackled two-objective problems (or formulations of problems), leaving much to be desired in terms of exploiting the power of MEOAs to discover solutions to intractable problems characterized by many trade-offs and complex constraints.

关键词： Multiobjective Optimization Pareto-Optimal Solutions search-based software engineering

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On the Value of User Preferences in search-based software engineering: A Case Study in software Product Lines

On the Value of User Preferences in Search-Based Software En...

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35th International Conference on software engineering (ICSE)

作者： Sayyad, Abdel Salam Menzies, Tim Ammar, Hany W Virginia Univ Lane Dept Comp Sci & Elect Engn Morgantown WV 26506 USA

ISBN: (纸本)9781467330763

software design is a process of trading off competing objectives. If the user objective space is rich, then we should use optimizers that can fully exploit that richness. For example, this study configures software product lines (expressed as feature maps) using various search-based software engineering methods. As we increase the number of optimization objectives, we find that methods in widespread use (e. g. NSGA-II, SPEA2) perform much worse than IBEA (Indicator-based Evolutionary Algorithm). IBEA works best since it makes most use of user preference knowledge. Hence it does better on the standard measures (hypervolume and spread) but it also generates far more products with 0% violations of domain constraints. Our conclusion is that we need to change our methods for search-based software engineering, particularly when studying complex decision spaces.

关键词： software Product Lines Feature Models Optimal Feature Selection Multiobjective Optimization Indicator-based Evolutionary Algorithm search-based software engineering

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Multi-objective improvement of Android applications

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AUTOMATED software engineering 2025年第1期32卷 1-36页

作者： Callan, James Petke, Justyna UCL Comp Sci Dept Gower St London WC1E 6BT England

Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. We propose a practical approach and the first open-source tool, GIDroid for multi-objective automated improvement of Android apps. In particular, we use Genetic Improvement, a search-based technique that navigates the space of software variants to find improved software. We use a simulation-based testing framework to greatly improve the speed of search. GIDroid contains three state-of-the-art multi-objective algorithms, and two new mutation operators, which cache the results of method calls. Genetic Improvement relies on testing to validate patches. Previous work showed that tests in open-source Android applications are scarce. We thus wrote tests for 21 versions of 7 Android apps, creating a new benchmark for performance improvements. We used GIDroid to improve versions of mobile apps where developers had previously found improvements to runtime, memory, and bandwidth use. Our technique automatically re-discovers 64% of existing improvements. We then applied our approach to current versions of software in which there were no known improvements. We were able to improve execution time by up to 35%, and memory use by up to 33% in these apps.

关键词： Android apps Genetic improvement Multi-objective optimization search-based software engineering

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Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning

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IEEE TRANSACTIONS ON software engineering 2025年第2期51卷 548-580页

作者： Chen, Pengzhou Gong, Jingzhi Chen, Tao Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610056 Peoples R China Univ Leeds Sch Comp Sci Leeds LS2 9JT England Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

To ease the expensive measurements during configuration tuning, it is natural to build a surrogate model as the replacement of the system, and thereby the configuration performance can be cheaply evaluated. Yet, a stereotype therein is that the higher the model accuracy, the better the tuning result would be, or vice versa. This "accuracy is all" belief drives our research community to build more and more accurate models and criticize a tuner for the inaccuracy of the model used. However, this practice raises some previously unaddressed questions, e.g., are the model and its accuracy really that important for the tuning result? Do those somewhat small accuracy improvements reported (e.g., a few % error reduction) in existing work really matter much to the tuners? What role does model accuracy play in the impact of tuning quality? To answer those related questions, in this paper, we conduct one of the largest-scale empirical studies to date-running over the period of 13 months 24x7-that covers 10 models, 17 tuners, and 29 systems from the existing works while under four different commonly used metrics, leading to 13,612 cases of investigation. Surprisingly, our key findings reveal that the accuracy can lie: there are a considerable number of cases where higher accuracy actually leads to no improvement in the tuning outcomes (up to 58% cases under certain setting), or even worse, it can degrade the tuning quality (up to 24% cases under certain setting). We also discover that the chosen models in most proposed tuners are sub-optimal and that the required % of accuracy change to significantly improve tuning quality varies according to the range of model accuracy. Deriving from the fitness landscape analysis, we provide in-depth discussions of the rationale behind, offering several lessons learned as well as insights for future opportunities. Most importantly, this work poses a clear message to the community: we should take one step back from the natural "accuracy is all

关键词： Accuracy Tuners Predictive models Computational modeling Throughput Systematics software systems Runtime Optimization Correlation search-based software engineering software configuration tuning performance optimization configurable systems model-based optimization heuristic algorithms hyperparameter tuning fitness landscape analysis

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MMO: Meta Multi-Objectivization for software Configuration Tuning

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IEEE TRANSACTIONS ON software engineering 2024年第6期50卷 1478-1504页

作者： Chen, Pengzhou Chen, Tao Li, Miqing Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Univ Birmingham Sch Comp Sci Birmingham B15 2TT England

software configuration tuning is essential for optimizing a given performance objective (e.g., minimizing latency). Yet, due to the software's intrinsically complex configuration landscape and expensive measurement, there has been a rather mild success, particularly in preventing the search from being trapped in local optima. To address this issue, in this paper we take a different perspective. Instead of focusing on improving the optimizer, we work on the level of optimization model and propose a meta multi-objectivization (MMO) model that considers an auxiliary performance objective (e.g., throughput in addition to latency). What makes this model distinct is that we do not optimize the auxiliary performance objective, but rather use it to make similarly-performing while different configurations less comparable (i.e. Pareto nondominated to each other), thus preventing the search from being trapped in local optima. Importantly, by designing a new normalization method, we show how to effectively use the MMO model without worrying about its weight-the only yet highly sensitive parameter that can affect its effectiveness. Experiments on 22 cases from 11 real-world software systems/environments confirm that our MMO model with the new normalization performs better than its state-of-the-art single-objective counterparts on 82% cases while achieving up to 2.09x speedup. For 68% of the cases, the new normalization also enables the MMO model to outperform the instance when using it with the normalization from our prior FSE work under pre-tuned best weights, saving a great amount of resources which would be otherwise necessary to find a good weight. We also demonstrate that the MMO model with the new normalization can consolidate recent model-based tuning tools on 68% of the cases with up to 1.22x speedup in general.

关键词： software Tuning search problems Throughput Optimization Storms software systems Configuration tuning performance optimization search-based software engineering multi-objectivization

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1st International Workshop on Combining Modelling and search-based software engineering (CMSBSE 2013)

1st International Workshop on Combining Modelling and Search...

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35th International Conference on software engineering (ICSE)

作者： Harman, Mark Paige, Richard F. Williams, James R. Univ York Dept Comp Sci Deramore Lane York YO10 5GH N Yorkshire England UCL CREST Ctr London WC1E 6BT England

ISBN: (纸本)9781467330763

Modelling plays a vital and pervasive role in software engineering: it provides means to manage complexity via abstraction, and enables the creation of larger, more complex systems. search-based software engineering (SBSE) offers a productive and proven approach to software engineering through automated discovery of near-optimal solutions to problems, and has proven itself to be effective on a wide variety of software-and systems engineering problems. CMSBSE 2013 was a forum allowing researchers from both communities to meet, discuss synergies and differences, and present topics related to the intersection of search and modelling. Particular goals of CMSBSE were to highlight that SBSE and modelling have substantial conceptual and technical synergy, and to identify and present opportunities in which they can be combined, whilst also aiming to grow the community working in this area.

关键词： Modelling search-based software engineering

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