Search-Based Software Engineering (SBSE) has been successfully applied to automate a wide range of software development activities. Nevertheless, in those software engineering problems where human evaluation and preference are crucial, such insights have proved difficult to characterize in search, and solutions might not look natural when that is the expectation. In an attempt to address this, an increasing number of researchers have reported the incorporation of the 'human-in-the-loop' during search and interactive SBSE has attracted significant attention recently. However, reported results are fragmented over different development phases, and a great variety of novel interactive approaches and algorithmic techniques have emerged. To better integrate these results, we have performed a systematic literature review of interactive SBSE. From a total of 669 papers, 26 primary studies were identified. To enable their analysis, we formulated a classification scheme focused on four crucial aspects of interactive search, i.e., the problem formulation, search technique, interactive approach, and the empirical framework. Our intention is that the classification scheme affords a methodological approach for interactive SBSE. Lastly, as well as providing a detailed cross analysis, we identify and discuss some open issues and potential future trends for the research community.

Early lifecycle software design is an intensely human activity in which design scale and complexity can place a high cognitive load on the software designer. Recently, the use of evolutionary search has been suggested to yield insights in the nature of software engineering problems generally, and so we have applied dynamic evolutionary computation using self-adaptive mutation to the object-oriented software design search space. Using three design problem instances of varying scale and complexity, initial investigations of the discrete search landscape reveal a redundancy in genotype-to-phenotype mapping enabling flexible and effective exploration. In further experiments, mutation probabilities and population diversity are observed to significantly increase in the face of increasing problem scale, but not for increasing complexity (in problems of the same scale). Based on these findings, we conclude that design problem scale rather than complexity has an effect on the software design process, emphasizing the role of decomposition as a design technique.

Recent research has demonstrated that the problem of class modelling within early cycle object orientated software engineering can be successfully tackled by posing it as a search problem to be tackled with meta-heuristics. This "Search Based Software Engineering" approach has been illustrated using both Evolutionary Algorithms and Ant Colony Optimisation to perform the underlying search. Each has been shown to display strengths and weaknesses - both in terms of how easily "standard" algorithms can be applied to the domain, and of optimisation performance. This paper extends that work by considering the effect of incorporating Local Search. Specifically we examine the hypothesis that within a memetic framework the choice of global search heuristic does not significantly affect search performance, freeing the decision to be made on other more subjective factors.

Design is fundamental to software development but can be demanding to perform. Thus, to assist the software designer, evolutionary computing is being increasingly applied using machine-based, quantitative fitness functions to evolve software designs. However, in nature, elegance and symmetry play a crucial role in the reproductive fitness of various organisms. In addition, subjective evaluation has also been exploited in interactive evolutionary computation (IEC). Therefore, to investigate the role of elegance and symmetry in software design, four novel elegance measures are proposed which are based on the evenness of distribution of design elements. In controlled experiments in a dynamic IEC environment, designers are presented with visualizations of object-oriented software designs, which they rank according to a subjective assessment of elegance. For three out of the four elegance measures proposed, it is found that a significant correlation exists between elegance values and reward elicited. These three elegance measures assess the evenness of distribution of 1) attributes and methods among classes; 2) external couples between classes; and 3) the ratio of attributes to methods. It is concluded that symmetrical elegance is in some way significant in software design, and that this can be exploited in dynamic, multiobjective IEC to produce elegant software designs.

User-centered Interactive Evolutionary Computation (IEC) has been applied to a wide variety of areas, including UML software design. The performance of evolutionary search is important as user interaction fatigue remains an on-going challenge in IEC. However, to obtain optimal search performance, it is usually necessary to “tune” evolutionary control parameters manually, although tuning control parameters can be time-consuming and error-prone. To address this issue in other fields of evolutionary computation, dynamic parameter control including deterministic, adaptive and self-adaptive mechanisms have been applied extensively to real-valued representations. This paper postulates that dynamic parameter control may be highly beneficial to IEC in general, and UML software design in particular, wherein a novel object-based solution representation is used. Three software design problems from differing design domains and of differing scale have been investigated with mutation probabilities modified by simulated annealing, the Rechenberg “1/5 success rule” and self-adaptation within local search. Results indicate that self-adaptation appears to be the most robust and scalable mutation probability modification mechanism. The use of self-adaption with an object-based representation is novel, and results indicate that dynamic parameter control offers great potential within IEC.

Although much evidence exists to suggest that early life cycle software engineering design is a difficult task for software engineers to perform, current computational tool support for software engineers is limited. To address this limitation, interactive search-based approaches using evolutionary computation and software agents are investigated in experimental upstream design episodes for two example design domains. Results show that interactive evolutionary search, supported by software agents, appears highly promising. As an open system, search is steered jointly by designer preferences and software agents. Directly traceable to the design problem domain, a mass of useful and interesting class designs is arrived at which may be visualized by the designer with quantitative measures of structural integrity, such as design coupling and class cohesion. The class designs are found to be of equivalent or better coupling and cohesion when compared to a manual class design for the example design domains, and by exploiting concurrent execution, the runtime performance of the software agents is highly favorable.

Conceptual software engineering design is an intensely people-oriented and non-trivial activity, yet current computational tool support is limited. While a number of search-based software engineering approaches to support software design have been reported, few empirical studies into their application have been described. This paper reports the findings of an observational study of conceptual design episodes in a UK higher education problem domain. When compared with a manual design episode, a design episode enabled by a user-interactive, search-based, evolutionary computation tool generates a large number of useful and interesting candidate designs, and provides enhanced qualitative and quantitative evaluation. It is also found that tool-supported visualization of UML class designs offers opportunities for sudden design discovery, and that designers respond positively to opportunities to explore and exploit multiple candidate designs. It appears therefore that search-based computational tool support offers high potential in the support of conceptual software engineering design.

Although much evidence exists to suggest that conceptual software engineering design is a difficult task for software engineers to perform, current computationally intelligent tool support for software engineers is limited. While search-based approaches involving module clustering and refactoring have been proposed and show promise, such approaches are downstream in terms of the software development lifecycle - the designer must manually produce a design before search-based clustering and refactoring can take place. Interactive, user-centered search-based approaches, on the other hand, support the designer at the beginning of, and during, conceptual software design, and are investigated in this paper by means of a case study. Results show that interactive evolutionary search, supported by software agents, appears highly promising. As an open system, search is steered jointly by designer preferences and software agents. Directly traceable to the design problem domain, a mass of useful and interesting conceptual class designs are arrived at which may be visualized by the designer with quantitative measures of structural integrity such as design coupling and class cohesion. The conceptual class designs are found to be of equivalent or better coupling and cohesion when compared to a manual conceptual design of the case study, and by exploiting concurrent execution, the performance of the software agents is highly favorable.

While recent attempts to search a conceptual software engineering design search space with multi-objective evolutionary algorithms have yielded promising results, the practical application of such search-based techniques remains to be addressed. This paper reports initial findings of the application of software agents in support of an interactive, user-centered conceptual software design scenario. The supporting role of a number of single responsibility agents is described and results for a case study indicate that the application of such agents to search-based design scenarios provides efficient, high performance and effective support. The notion of interactive, joint human-computer activity appears to map well to conceptual software design scenarios: focus on superior design concepts and thence to useful and interesting designs provides a natural and effective way of narrowing the population based search. In addition, agents and the human designer appear to interact as cooperative "team players", jointly influencing the evolutionary algorithm based search. Nevertheless, challenges remain, including expanding the scale of implementation of underlying technologies to support distributed, collaborative design.

This poster paper investigates the potential of single and multi-objective genetic operators with an object-oriented conceptual design space. Using cohesion as an objective fitness function, genetic operators inspired by genetic algorithms and evolutionary programming are compared against a simple case study. Also, using both cohesion and coupling as objective fitness functions, multi-objective genetic operators inspired by a non-dominated sorting algorithm have been developed. Cohesion and coupling values achieved are similar to human performed designs and a large number and variety of optimal solutions are arrived at, which could not have been produced by the human software engineer. We conclude that this mass of optimal design variants offers significant potential for design support when integrated with user-centric, computationally intelligent tools.