Deep learning (DL) techniques have grown in leaps and bounds in both academia and industry over the past few years. Despite the growth of DL projects, there has been little study on how DL projects evolve, whether maintainers in this domain encounter a dramatic increase in workload, and whether or not existing maintainers can guarantee the sustained development of projects. To address this gap, we perform an empirical study to investigate the sustainability of DL projects, understand maintainers’ workloads and workloads growth in DL projects, and compare them with traditional OSS projects. In this regard, we first investigate how DL projects grow, then, understand maintainers’ workload in DL projects, and explore the workload growth of maintainers as DL projects evolve. After that, we mine the relationships between maintainers’ activities and the sustainability of DL projects. Eventually, we compare it with traditional OSS projects. Our study unveils that although DL projects show increasing trends in most activities, maintainers’ workloads present a decreasing trend. Meanwhile, the proportion of workload maintainers conducted in DL projects is significantly lower than in traditional OSS projects. Moreover, there are positive and moderate correlations between the sustainability of DL projects and the number of maintainers’ releases, pushes, and merged pull requests. Our findings shed lights that help understand maintainers’ workload and growth trends in DL and traditional OSS projects, and also highlight actionable directions for organizations, maintainers, and researchers.
On June 26-28, 2020, the International Conference on Software and Systems Processes (ICSSP 2020) and the International Conference on Global Software Engineering (ICGSE 2020) were held in virtual settings during the first year of the COVID pandemic. Several submissions to the joint event have been selected for inclusion in this special issue, focusing on impactful and timely contributions to Machine Learning (ML). At present, many in our field are enthusiastic about the potential of ML, yet some risks should not be casually overlooked or summarily dismissed. Each ML implementation is subtly different from any other implementation, and the risk profile varies greatly based on the approach adopted and the implementation context. The ICSSP/ICGSE 2020 Program Committees have encouraged submissions that explore the risks and benefits associated with ML so that the important discussion regarding ML efficacy and advocacy can be further elaborated. Four contributions have been included in this special issue.
Developers spend significant time locating and fixing bugs, which is often performed manually. Although spectrum-based fault localization (SFL) techniques aim at helping developers to locate faults, they are not yet used in practice. Recent studies have investigated how developers use SFL, presenting different conclusions about their effectiveness and usefulness. We carried out a user study to further enhance the understanding of SFL. We assessed whether SFL improves the developers’ performance and to what extent SFL leads developers to inspect faulty code excerpts. We also investigated the intention of the developers to use SFL and how they interact with SFL. Twenty-six participants performed debugging tasks using real programs, with and without using the Jaguar SFL tool. Using SFL, more developers located and fixed the bugs. SFL also led more developers to inspect the faulty code and locate the faulty method. However, they did not spend less time locating the faults. SFL was well accepted by the participants, who showed intention to use it in their daily activities. Our results indicate that SFL is useful even when the fault is not ranked among the first positions, leading developers to reach faulty code regions and find the bugs.
To enable the people involved in a software development process to communicate and reason close to their area of knowledge, we are investigating a method to formalize and integrate knowledge of multiple domains into domain models and into specifications in terms of those domain models. For this purpose, we have previously defined a set of method objectives, and an initial version of the method –called MuDForM. This paper reports on the methodical support for using a domain model as terminology to define other specifications, and feature specifications in particular. We performed a case study to validate how well the method helps in the specification of processes and to realize the case-specific objectives of the customer. The case study pertains to the formalization of the ISO26262 standard for functional safety in the automotive domain. We found that our method is suitable to systematically formalize a process that is described in natural language, such that there is a clear separation of domain-specific concepts, unambiguous process specifications, and concepts from outside the domain and process of interest. We have extended our method with concepts, steps, and guidelines for grammatical analysis, for the formalization of constraints, and for the specification of processes. The case-specific results are the unambiguous specification of a part of the ISO26262 processes.