Abstract The increasing complexity of software development has prompted a shift in project management practices toward agile methodologies, often supported by specialized software tools for planning and reporting. However, many widely adopted tools offer only limited decision support capabilities. This work introduces ProjectION, an intelligent software tool designed to enhance decision-making in agile project management through techniques relative to the Search-Based Software Engineering field. Based on an in-depth analysis of the challenges faced in agile environments, ProjectION assists decision makers in monitoring project status, forecasting progress, and automating software release planning. A comprehensive usability study was conducted with a small sample of experienced IT professionals, following the ISO/IEC 25062:2006 Common Industry Format. Key usability metrics of effectiveness, efficiency, and user satisfaction were assessed. Despite minor usability issues, results indicate that ProjectION effectively supports agile release planning. A second experiment further demonstrates the tool’s utility in generating optimal release plans, outperforming manual solutions proposed by decision makers. To foster collaboration and future development, the core algorithms and execution service have been made publicly available, enabling integration of novel approaches to the Next Release Problem.

Agile methodologies are widely adopted in the industry, with iterative development being a common practice. However, this approach introduces certain risks in controlling and managing the planned scope for delivery at the end of each iteration. Previous studies have proposed machine learning methods to predict the likelihood of meeting this committed scope, using models trained on features extracted from prior iterations and their associated tasks. A crucial aspect of any predictive model is user trust, which depends on the model’s explainability. However, an excessive number of features can complicate interpretation. In this work, we propose feature subset selection methods to reduce the number of features without compromising model performance.To ensure interpretability, we leverage state-of-the-art explainability techniques to analyze the key features driving model predictions. Our evaluation, conducted on five large open-source projects from prior studies, demonstrates successful feature subset selection, reducing the feature set to 10% of its original size without any loss in predictive performance. Using explainability tools, we provide a synthesis of the features with the most significant impact on iteration performance predictions across agile projects.

Release planning is a critical step in the development of a software product, and it involves many factors. Deciding what to build for the next software release requires taking into account not only the cost of building a subset of software features, but also the expected satisfaction of the clients, as well as the dependencies that the features might have among them. This problem, called Next Release Problem, can be difficult to tackle by expert judge, or even intractable if the number of requirements, dependencies and clients to consider is very large. In the literature, this problem has been approached from the so-called search-based software engineering field, introducing a variety of metaheuristic algorithms to obtain a subset of release proposals that simultaneously optimise both cost and satisfaction. In this work, we present a GRASP-based advanced method, and evaluate it against other families of algorithms commonly applied to this problem, using two public and four synthetic datasets for the evaluation. Results show that solutions obtained by our proposal are superior to those of other algorithms in terms of quality indicators and speed of execution. Algorithms, datasets and evaluation framework have been made available to the research community.

In the development of a software product, the Next Release Problem is the selection of the most appropriate subset of requirements (tasks) to include in the next release of the product, such that the selected subset maximises the overall satisfaction of the stakeholders and minimises the total cost. Furthermore, in most cases, requirements or tasks cannot be developed independently, as there are dependencies between them, which must be respected in the selection for the next release. In this paper, we approach the Next Release Problem as a constrained bi-objective optimisation problem. The main contribution is the design of an Estimation of Distribution Algorithm that exploits domain knowledge, i.e. the dependencies between the requirements, to define the structure of a Bayesian network that models the relationships between the binary variables (requirements) to be optimised. The use of a Bayesian network with a fixed structure reduces the complexity of the search process, since it is unnecessary to learn the structure at each iteration of the algorithm. Moreover, it ensures that the sampled individuals are always valid with respect to the required dependencies. The second main contribution is the generation of a corpus of synthetic datasets with cost estimations derived from agile and classic management methodologies. Standard multi-objective metrics are computed in order to assess our proposal and compare it with other evolutionary multi-criterion optimisation algorithms, determining that it is the optimal choice when dealing with complex datasets.

Abstract DevOps has been identified by industry as one of the cornerstones of their development process. It is not just a set of tools but a set of principles and practices to build an efficient team improving the communication and collaboration. Its importance has greatly impacted even hiring processes being DevOps Engineer among the most recruited jobs according to LinkedIn. But it is not clear whether universities have noticed the magnitude this movement has attained in industry, despite the need of higher education and industry building up advances together. This led us to determine whether there is a gap between the training provided by higher education and the one expected from industry. For this aim, a questionnaire, defined after a careful review of the literature, has been run worldwide to answer the four research questions. The analysis arose several conclusions, such as higher prevalence of use than of training for most of the analyzed technological practices, except for those related to architecture, probably due to the migration cost these require. It was also found that the two practices with higher prevalence in industry, feedback and limit‐WIP, are scarcely trained in higher education. These conclusions provide interesting advice for future adaptations of computer science degrees.

Abstract The multilabel paradigm has recently attracted the attention of the machine learning community, multilabel problems being those which do not have only one class but several binomial classes instead. Although intensive research has been carried on lately into the multilabel classification paradigm, this is not the case of feature subset selection methods. In this work, we propose an adaptation of the well‐known CMIM feature selection algorithm, which is capable of approximating the conditional multivariate mutual information of each candidate attribute with respect to the whole set of labels. This capacity to search any degree of interaction among labels is the reason why our proposal performs better than other state‐of‐the‐art algorithms when the dataset on which it is run contains correlated labels.

There exist a wide number of works in the literature related to new systems devoted to manage thermal control in buildings. Commonly, their evaluation is performed by using simulation of users and environmental conditions. Thus, in this work we choose a successful thermal-comfort system, formerly evaluated with simulations, and evaluate it by using data from project ASHRAE RP-884, which provides logs of real data coming from different buildings, in a wide variety of climates, and occupied by people with different thermal preferences. From these logs, we propose a pre-processing and evaluation methodology in order to achieve more realistic evaluations.

Feature subset selection has become an expensive process due to the relatively recent appearance of high-dimensional databases. Thus, not only the need has arisen for reducing the dimensionality of these datasets, but also for doing it in an efficient way. We propose a new backward search, where attributes are removed given several smart criteria found in the literature and, besides, it is guided using a heuristic which reduces the cost and needed number of evaluations commonly expected from a backward search. Besides, we do not only propose the design of a new forward-backward algorithm but we also provide an experimental study of different criteria to decide the removal of attributes. The result is a very competitive algorithm which does not exceed the in-practice linear complexity while obtaining selected subsets of features with lower cardinality than other state-of-the-art algorithms.

This paper deals with the problem of feature subset selection in classification-oriented datasets with a (very) large number of attributes. In such datasets complex classical wrapper approaches become intractable due to the high number of wrapper evaluations to be carried out. One way to alleviate this problem is to use the so-called filter-wrapper approach or Incremental Wrapper-based Subset Selection (IWSS), which consists of the construction of a ranking among the predictive attributes by using a filter measure, and then a wrapper approach is used by following the rank. In this way the number of wrapper evaluations is linear on the number of predictive attributes. In this paper we present two contributions to the IWSS approach. The first one is related with obtaining more compact subsets, and enables not only the addition of new attributes but also their interchange with some of those already included in the selected subset. Our second contribution, termed early stopping, sets an adaptive threshold on the number of attributes in the ranking to be considered. The advantages of these new approaches are analyzed both theoretically and experimentally. The results over a set of 12 high-dimensional datasets corroborate the success of our proposals.

This paper deals with the problem of wrapper-based feature subset selection in classification oriented datasets with a (very) large number of attributes. In such datasets sophisticated search algorithms like beam search, branch and bound, best first, genetic algorithms, etc., become intractable in the wrapper approach due to the high number of wrapper evaluations to be carried out. One way to alleviate this problem is to use the so-called filter-wrapper approach or Incremental Wrapper-based Subset Selection (IWSS), which consists in the construction of a ranking among the predictive attributes by using a filter measure, and then a wrapper approach is used guided by the rank. In this way the number of wrapper evaluations is linear with the number of predictive attributes. In this paper we present a contribution to the IWSS approach which helps it to obtain more compact subsets, and consists into allow not only the addition of new attributes but also the interchange with some of the already included in the selected subset. The disadvantage of this novelty is that it grows up the worst-case complexity of IWSS up to O(n2), however, as in the case of the well known sequential forward selection (SFS) the actual number of wrapper evaluations is considerably smaller. Empirical tests over 7 (biological) datasets with a large number of attributes demonstrate the success of the proposed approach when comparing with both IWSS and SFS.