The widespread use of machine learning (ML) in software engineering (SE) encounters a notable challenge: the need for various domain-specific parameters in algorithms. The issue arises when attempting to reuse these parameters across different applications, resulting in sub-optimal outcomes. This hindrance significantly contributes to the limited migration of ML solutions from research labs to industrial settings. This paper underscores the pressing need for novel research to tackle the overarching problem of generic algorithm customisation. To address this, we propose leveraging Automated Machine Learning (AutoML) approaches. These techniques automatically select intelligible models and their corresponding hyper-parameters for forecasting software defect-proneness. More specifically, this paper adapts an AutoML approach to the field of software defect prediction, namely a hyper-heuristic evolutionary algorithm for automatically designing decision tree algorithms (HEAD-DT), originally proposed to address a generic optimisation problem. We benchmark against the popular general software defect-proneness prediction framework (GSDP) and some standard classifiers. Experimental results reveal that the proposed HEAD-DT implementation surpasses other algorithms across three distinct evaluation measures.

The integration of Agile software development and User Experience (UX) has become a growing field of research, as both approaches play critical roles in building digital products and services. In this special issue on Agile UX, the current state of the field is explored through a combination of systematic literature reviews and qualitative and quantitative studies. The special issue provide an overview of the key trends, challenges, and successes in combining Agile and UX, and highlight the importance of involving stakeholders throughout development. The shift from plan-driven approaches to Agile UX approaches has brought a focus on human values and a better understanding of the importance of considering users’ needs. We present recent advances in research and practice, showing that Agile UX is a continuous journey towards changing user behavior by delivering value.

Software analytics supports data-driven decision making, which allows software practitioners to leverage valuable insights from software data to improve their processes and many quality aspects of the software.In this paper, we present an artifact designed from a set of patterns to support agile teams to plan and manage software analysis activities, named Software Analytics Canvas.Further, we report the study undertaken to evaluate the ease of use and the utility of our canvas from the practitioners' viewpoint, and a participatory design session carried out to collect information about possible artifact improvements.In general, subjects found the artifact useful, but some of them reported difficulties in learning and understanding how to use it.In the participatory design, they pointed out improvement points and a new layout for the canvas components.The results of both studies helped us refine the proposed artifact, improving both the terms used in each element and the layout of the blocks to make more sense for its users.

Abstract One of the main challenges faced while establishing the integration of Agile and User-Centered Design is how to facilitate communication among the invariably distinct involved practitioners. Advocating the use of artifacts as enablers in this scenario, this paper aims to explore and understand the artifacts which can facilitate the communication between developers and designers in an Agile User-Centered Design approach. Drawing upon a netnography of a globally-distributed online community, we carried out community observation, data collection, and data analysis. The data analysis and interpretation pointed out two major themes: artifacts facilitate communication and artifacts support collaboration. Our paper provides an overview of the artifacts used for communication in Agile User-Centered Design and highlights how artifact-facilitated communication ensues in the industry through a perspective from practitioners.

Abstract Test-Driven Development (TDD) is one of the most popular agile practices among software developers. To investigate the software developers’ initial perceptions when applying TDD, we have performed an exploratory study. This study was carried out with participants who had about ten years of professional experience (on average), the majority of whom with no experience using TDD. The study is in the context of an agile project course at the postgraduate level of a research institute. Participants individually developed medium size projects addressed to different domains and using different programming languages. Through a structured questionnaire with open and semi-open questions, we collected information on TDD effects such as the perceived benefits, encountered difficulties, and developer’s opinion about the quality improvement of the software. Afterward, we conducted a thematic analysis of the qualitative data. Most participants noticed improvements in code quality, but few have a more comprehensive view of the effects of TDD on software design. Our findings suggest that after overcoming the initial difficulties to understand where to start, and know how to create a test for a feature that does not yet exist, participants gain greater confidence to implement new features and make changes due to broad test coverage.

Software effort estimation is an important task within software engineering. It is widely used for planning and monitoring software project development as a means to deliver the product on time and within budget. Several approaches for generating predictive models from collected metrics have been proposed throughout the years. Machine learning algorithms, in particular, have been widely-employed to this task, bearing in mind their capability of providing accurate predictive models for the analysis of project stakeholders. In this paper, we propose a grammatical evolution approach for software metrics estimation. Our novel algorithm, namely SEEGE, is empirically evaluated on public project data sets, and we compare its performance with state-of-the-art machine learning algorithms such as support vector machines for regression and artificial neural networks, and also to popular linear regression. Results show that SEEGE outperforms the other algorithms considering three different evaluation measures, clearly indicating its effectiveness for the effort estimation task.