Abstract Correlated quality metrics extracted from a source code repository can be utilized to design a model to automatically predict defects in a software system. It is obvious that the extracted metrics will result in a highly unbalanced data, since the number of defects in a good quality software system should be far less than the number of normal instances. It is also a fact that the selection of the best discriminating features significantly improves the robustness and accuracy of a prediction model. Therefore, the contribution of this paper is twofold, first it selects the best discriminating features that help in accurately predicting a defect in a software component. Secondly, a cost-sensitive logistic regression and decision tree ensemble-based prediction models are applied to the best discriminating features for precisely predicting a defect in a software component. The proposed models are compared with the most recent schemes in the literature in terms of accuracy, area under the curve, and recall. The models are evaluated using 11 datasets and it is evident from the results and analysis that the performance of the proposed prediction models outperforms the schemes in the literature.

Abstract Sustainable living, ie, living within the bounds of the available environmental, social, and economic resources, is the focus of many present‐day social and scientific discussions. But what does sustainability mean within the context of software engineering? In this paper, we undertake a comprehensive analysis of 8 case studies to address this question within the context of a specific software engineering approach, software product line engineering (SPLE). We identify the sustainability‐related characteristics that arise in present‐day studies that apply SPLE. We conclude that technical and economic sustainability are in prime focus on the present SPLE practice, with social sustainability issues, where they relate to organisations, also addressed to a good degree. On the other hand, the issues related to the personal sustainability are less prominent, and environmental considerations are nearly completely amiss. We present feature models and cross‐relations that result from our analysis as a starting point for sustainability engineering through SPLE, suggesting that any new development should consider how these models would be instantiated and expanded for the intended sociotechnical system. The good representation of sustainability features in these models is also validated with 2 additional case studies.

Sustainability encompasses a wide set of aims: ranging from energy efficient software products (environmental sustainability), reduction of software development and maintenance costs (economic sustainability), to employee wellbeing (social sustainability). This panel brings together researchers and practitioners to explore the role that sustainability will play in software product line engineering. The panel aims to explore how sustainability manifests itself in domain engineering, via study of, for instance, sustainability patterns in domain analysis, architectural decisions motivated by specific sustainability concerns, types of variability that results from sustainability considerations, as well as engineering of sustainability as a domain itself. This panel explores challenges in research and practice for Sustainability in Software Product Line Engineering.

The requirements and design level identification and representation of dynamic variability for adaptive systems is a challenging task. This requires time and effort to identify and model the relevant elements as well as the need to consider the large number of potentially possible system configurations. Typically, each individual variability dimension needs to identified and modelled by enumerating each possible alternative. The full set of requirements needs to be reviewed to extract all potential variability dimensions. Moreover, each possible configuration of an adaptive system needs to be validated before use. In this demonstration, we present a tool suite that is able to manage dynamic variability in adaptive systems and tame such system complexity. This tool suite is able to automatically identify dynamic variability attributes such as variability dimensions, context, adaptation rules, and soft/hard goals from requirements documents. It also supports modelling of these artefacts as well as their run-time verification and validation.

Due to the very large number of configurations that can typically be derived from a Dynamic Software Product Line (DSPL), efficient and effective testing of such systems have become a major challenge for software developers. In particular, when a configuration needs to be deployed quickly due to rapid contextual changes (e.g., in an unfolding crisis), time constraints hinder the proper testing of such a configuration. In this paper, we propose to reduce the testing required of such DSPLs to a relevant subset of configurations. Whenever a need to adapt to an untested configuration is encountered, our approach determines the most similar tested configuration and reuses its test results to either obtain a coverage measure or infer a confidence degree for the new, untested configuration. We focus on providing these techniques for inference of structural testing results for DSPLs, which is supported by an early prototype implementation.

Delivering software systems that fulfill all requirements of the stakeholders is very difficult, if not at all impossible. We consider the problem of coping with imperfect information, like interpreting incomplete requirement specifications or vagueness in decisions, one of the main reasons that makes software design difficult. We define a method for tracing design decisions under imperfect information. To model and compare requirements with estimations, we present fuzzy and stochastic techniques. This approach offers adequate decision support that can deal with imperfect information during software design. The approach is illustrated by a real-world example, based on a storm surge barrier system.