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.

Bug localization, which is used to help programmers identify the location of bugs in source code, is an essential task in software development. Researchers have already made efforts to harness the powerful deep learning (DL) techniques to automate it. However, training bug localization model is usually challenging because it requires a large quantity of data labeled with the bug’s exact location, which is difficult and time-consuming to collect. By contrast, obtaining bug detection data with binary labels of whether there is a bug in the source code is much simpler. This paper proposes a WEakly supervised bug LocaLization (WELL) method, which only uses the bug detection data with binary labels to train a bug localization model. With CodeBERT finetuned on the buggy-or-not binary labeled data, WELL can address bug localization in a weakly supervised manner. The evaluations on three method-level synthetic datasets and one file-level real-world dataset show that WELL is significantly better than the existing SOTA model in typical bug localization tasks such as variable misuse and other programming bugs.

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.

The extit{instanceof} pattern matching is periodically previewed in the latest published JDK by removing the redundant casting and optimizing its usage in different scenarios, which improves the code quality and readability. However, the existing integrated development environment (IDE) does not provide sufficient support for refactoring of extit{instanceof} pattern matching in all situations. This paper first identifies several cases that cannot be handled by existing IDEs, and then proposes a novel approach called extit{ReInstancer} to refactor extit{instanceof} pattern matching automatically. extit{ReInstancer} conducts visitor pattern analysis for extit{if} statement blocks with extit{instanceof}. For the extit{instanceof} expression, a pattern variable is obtained after removing the redundant type casting by pattern analysis. For multi-branch statements, extit{ReInstancer} employs class hierarchy analysis and control flow analysis to infer the relationship of instance type in each branch and then optimize these branches. Finally, the multi-branch statements are converted into switch statements or switch expressions. extit{ReInstancer} is evaluated with 20 real-world applications. The experimental results demonstrate a total of 3558 extit{instanceof} expressions and 228 multi-branch statements applying the refactoring, and each application takes an average of 10.8s. extit{ReInstancer} not only reduces the redundant type casting but also improves the code quality.

The Physics of Notations (PoN) is a framework to design cognitively effective notations. The term cognitive effectiveness here refers to the ease (speed and accuracy) by which model readers read the models. In 2019 a PoN-Enabled notation for use case diagrams was proposed. It has been empirically proven that the cognitive effectiveness of this new notation is superior to the original use case notation. The new notation however relies in part on the use of color. The accessibility of this new design however has not been validated for use by the color-blind community, or any users who need to use a greyscale version of the models created. The PoN framework was not designed to explicitly account for the color-blind community or users who can only access greyscale versions of the models. Inclusivity should not be an afterthought in software engineering practice or research. To this end, this paper presents an empirical evaluation of the cognitive effectiveness of the PoN-enabled use case notation in the situation it was viewed in greyscale.

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.