A Project Scheduling Problem consists in deciding who does what during the software project lifetime. This is a capital application in the practice of software engineering, since the total budget and human resources involved must be managed optimally in order to end in a successful project. In short, companies are principally concerned with reducing the duration and cost of a project, and these two goals are in conflict with each other. In this work we tackle the problem by using genetic algorithms (GAs) to solve many different software project scenarios. Thanks to our newly developed instance generator we can perform structured studies about the influence the most important attributes of the problem have on the solutions. Our conclusions show that GAs are quite flexible and accurate for this application, and an important tool for automatic project management.

Hill climbing algorithms are at the core of many approaches to solve optimization problems. Such algorithms usually require the complete enumeration of a neighborhood of the current solution. In the case of problems defined over binary strings of length n, we define the r-ball neighborhood as the set of solutions at Hamming distance r or less from the current solution. For r n this neighborhood contains Θ(n r ) solutions. In this paper efficient methods are introduced to locate improving moves in the r-ball neighborhood for problems that can be written as a sum of a linear number of subfunctions depending on a bounded number of variables. NK-landscapes and MAX-kSAT are examples of these problems. If the number of subfunctions depending on any given variable is also bounded, then we prove that the method can explore the neighborhood in constant time, despite the fact that the number of solutions in the neighborhood is polynomial in n. We develop a hill climber based on our exploration method and we analyze its efficiency and efficacy using experiments with NKq-landscapes instances.

Radio network design (RND) is a basic problem in cellular networks for telecommunications. In these networks, the terrain must be covered by a set of base stations (or antennae), each of which defines a covering area called cell. The problem may be reduced to figure out the optimal placement of antennae on candidate sites trying to satisfy two objectives: to maximize the area covered by the radio signal and to reduce the number of used antennae. Consequently, RND is a bi-objective optimization problem. Previous works have solved the problem by using single-objective techniques which combine the values of both objectives. The used techniques have allowed to find optimal solutions according to the defined objective, thus yielding a unique solution instead of the set of Pareto optimal solutions. In this paper, we solve the RND problem using a multi-objective version of the algorithm CHC, which is the metaheuristic having reported the best results when solving the single-objective formulation of RND. This new algorithm, called MOCHC, is compared against a binary-coded NSGA-II algorithm and also against the provided results in the literature. Our experiments indicate that MOCHC outperfoms NSGA-II and, more importantly, it is more efficient finding the optimal solutions than single-objectives techniques.

The energy consumption of mobile apps is a trending topic and researchers are actively investigating the role of coding practices on energy consumption. Recent studies suggest that design choices can conflict with energy consumption. Therefore, it is important to take into account energy consumption when evolving the design of a mobile app. In this paper, we analyze the impact of eight type of anti-patterns on a testbed of 20 android apps extracted from F-Droid. We propose EARMO, a novel anti-pattern correction approach that accounts for energy consumption when refactoring mobile anti-patterns. We evaluate EARMO using three multiobjective search-based algorithms. The obtained results show that EARMO can generate refactoring recommendations in less than a minute, and remove a median of 84% of anti-patterns. Moreover, EARMO extended the battery life of a mobile phone by up to 29 minutes when running in isolation a refactored multimedia app with default settings (no WiFi, no location services, and minimum screen brightness). Finally, we conducted a qualitative study with developers of our studied apps, to assess the refactoring recommendations made by EARMO. Developers found 68% of refactorings suggested by EARMO to be very relevant.

SUMMARYAutomatic Test Data Generation is a very popular domain in the field of Search Based Software Engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, like the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behaviour. Indeed, in very large software systems, the cost spent to test the system can be an issue, and then it makes sense considering two conflicting objectives: maximizing the coverage and minimizing the oracle cost. This is what we do in this paper. We mainly compare two approaches to deal with the Multi-Objective Test Data Generation Problem: a direct multi-objective approach and a combination of a mono-objective algorithm together with multi-objective test case selection optimization. Concretely, in this work we use four state-of-the-art multi-objective algorithms and two mono-objective evolutionary algorithms followed by a multi-objective test case selection based on Pareto efficiency. The experimental analysis compares these techniques on two different benchmarks. The first one is composed by 800 java programs created through a program generator. The second benchmark is composed by 13 real programs extracted from the literature. In the direct multi-objective approach, the results indicate that the oracle cost can be properly optimized; however the full branch coverage of the system poses a great challenge. Regarding the monoobjective algorithms, although they need a second phase of test case selection for reducing the oracle cost, they are very effective maximizing the branch coverage.