Search-Based Software Engineering in the Era of Modern Software Systems

Tony Fan C. Menghi Federico Formica

22 Juli 2022

Search-based software testing (SBST) typically relies on fitness functions to guide the search exploration toward software failures. There are two main techniques to define fitness functions: (a) automated fitness function computation from the specification of the system requirements, and (b) manual fitness function design. Both techniques have advantages. The former uses information from the system requirements to guide the search toward portions of the input domain more likely to contain failures. The latter uses the engineers’ domain knowledge. We propose ATheNA, a novel SBST framework that combines fitness functions automatically generated from requirements specifications and those manually defined by engineers. We design and implement ATheNA-S, an instance of ATheNA that targets Simulink® models. We evaluate ATheNA-S by considering a large set of models from different domains. Our results show that ATheNA-S generates more failure-revealing test cases than existing baseline tools and that the difference between the runtime performance of ATheNA-S and the baseline tools is not statistically significant. We also assess whether ATheNA-S could generate failure-revealing test cases when applied to two representative case studies: one from the automotive domain and one from the medical domain. Our results show that ATheNA-S successfully revealed a requirement violation in our case studies.

Tao Chen Miqing Li

8 Februari 2022

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem’s Pareto-optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights that reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice, since it simplifies the search via converting the original multi-objective problem into a single-objective one and enables the search to focus on what only the stakeholders are interested in. This article questions such a “weighted search first” belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large-scale empirical study that consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is significantly better than its weighted counterpart the majority of the time (up to 77% of the cases), as long as we allow a sufficient, but not unrealistic search budget. This is a beneficial result, as it discovers a potentially new “rule-of-thumb” for the SBSE community: Even when clear preferences are available, it is recommended to always consider Pareto search by default for multi-objective SBSE problems, provided that solution quality is more important. Weighted search, in contrast, should only be preferred when the resource/search budget is limited, especially for expensive SBSE problems. This, together with other findings and actionable suggestions in the article, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. All code and data can be accessed at https://github.com/ideas-labo/pareto-vs-weight-for-sbse.

Neelofar A. Aleti K. Smith‐Miles + 1 lainnya

1 April 2023

Search-based software testing (SBST) is now a mature area, with numerous techniques developed to tackle the challenging task of software testing. SBST techniques have shown promising results and have been successfully applied in the industry to automatically generate test cases for large and complex software systems. Their effectiveness, however, has been shown to be problem dependent. In this paper, we revisit the problem of objective performance evaluation of SBST techniques in light of recent methodological advances – in the form of Instance Space Analysis (ISA) – enabling the strengths and weaknesses of SBST techniques to be visualised and assessed across the broadest possible space of problem instances (software classes) from common benchmark datasets. We identify features of SBST problems that explain why a particular instance is hard for an SBST technique, reveal areas of hard and easy problems in the instance space of existing benchmark datasets, and identify the strengths and weaknesses of state-of-the-art SBST techniques. In addition, we examine the diversity and quality of common benchmark datasets used in experimental evaluations.

Yibiao Yang Zuohua Ding Yuming Zhou + 1 lainnya

1 Juni 2022

Software refactoring is one of the most significant practices in software maintenance as the quality of software design tends to deteriorate during software evolution. But, refactoring software is a very challenging task as it requires a holistic view of the entire software system. To this end, recent studies introduced search-based algorithms to facilitate software refactoring. However, they still have the following major limitations: 1) the searched solutions may violate the design principles as their fitness functions do not directly reflect the degree of software’s compliance with design principles; 2) most approaches start the searching process from a completely random initial population, which may lead to unoptimal solutions. In this article, we aim to develop effective search-based refactoring approach to recommend better refactoring activities for developers which can improve the degree of software’s compliance with design principles as well as the software design quality. We propose DEPICTER, a design-principle guided and heuristic-rule constrained software refactoring recommendation approach. In particular, DEPICTER uses non-dominated sorting genetic algorithm (NSGA)-II genetic algorithm and employs design-principle metrics as fitness functions. Besides, DEPICTER leverages heuristic rules to improve the quality of initial population for subsequent generic evolution. Our evaluations, based on four widely used systems, show that DEPICTER is effective for guiding the development of better refactoring models in practice.

A. Rainer Zhengyang Li

1 November 2022

Academic search engines (i.e., digital libraries and indexers) play an increasingly important role in systematic reviews however these engines do not seem to effectively support such reviews, e.g., researchers confront usability issues with the engines when conducting their searches. To investigate whether the usability issues are bugs (i.e., faults in the search engines) or constraints, and to provide recommendations to search-engine providers and researchers on how to tackle these issues. Using snowball-sampling from tertiary studies, we identify a set of 621 secondary studies in software engineering. By physically re-attempting the searches for all of these 621 studies, we effectively conduct regression testing for 42 search engines. We identify 13 bugs for eight engines, and also identify other constraints. We provide recommendations for tackling these issues. There is still a considerable gap between the search-needs of researchers and the usability of academic search engines. It is not clear whether search-engine developers are aware of this gap. Also, the evaluation, by academics, of academic search engines has not kept pace with the development, by search-engine providers, of those search engines. Thus, the gap between evaluation and development makes it harder to properly understand the gap between the search-needs of researchers and search-features of the search engines.