Journal of Software-Evolution and Process最新文献

Issue tracking systems are now prevalent in software development, which would help developers submit and discuss issues to solve development problems on software projects. Most previous studies have been conducted to analyze issue relations within projects, such as recommending similar or duplicate bug issues. However, along with the popularization of co-developing through multiple projects, many issues are cross-project correlated (CPC), that is, one issue is associated with another issue in a different project. When developers meet with CPC issues, it may primarily increase the difficulties of solving them because they need information from not only their projects but also other related projects that developers are not familiar with. Identifying a CPC issue as early as possible is a fundamental challenge for both managers and developers to allocate the resources for software maintenance and estimate the effort to solve it. This paper proposes 11 issue metrics of two groups to describe textual summary and reporters' activity, which can be extracted just after the issue was reported. We employ these 11 issue metrics to construct just-in-time (JIT) prediction models to identify CPC issues. To evaluate the effect of CPC issue prediction models, we conduct experiments on 16 open-source data science and deep learning projects and compare our prediction model with two baseline models based on textual features (i.e., Term Frequency-Inverse Document Frequency [TF-IDF] and Word Embedding), which are commonly adopted by previous studies on issue prediction. The results show that the JIT prediction model based on issue metrics has significantly improved the performance of CPC issue prediction under two evaluation indicators, Matthew's correlation coefficient (MCC) and F1. In addition, we find that the prediction model is more suitable for large-scale complex core projects in the open-source ecosystem.

Recent advances in artificial intelligence technology and perception components have promoted the rapid development of autonomous vehicles. However, as safety-critical software, autonomous driving systems often make wrong judgments, seriously threatening human and property safety. LiDAR is one of the most critical sensors in autonomous vehicles, capable of accurately perceiving the three-dimensional information of the environment. Nevertheless, the high cost of manually collecting and labeling point cloud data leads to a dearth of testing methods for LiDAR-based perception modules. To bridge the critical gap, we introduce MetaLiDAR, a novel automated metamorphic testing methodology for LiDAR-based autonomous driving systems. First, we propose three object-level metamorphic relations for the domain characteristics of autonomous driving systems. Next, we design three transformation modules so that MetaLiDAR can generate natural-looking follow-up point clouds. Finally, we define corresponding evaluation metrics based on metamorphic relations. MetaLiDAR automatically determines whether source and follow-up test cases meet the metamorphic relations based on the evaluation metrics. Our empirical research on five state-of-the-art LiDAR-based object detection models shows that MetaLiDAR can not only generate natural-looking test point clouds to detect 181,547 inconsistent behaviors of different models but also significantly enhance the robustness of models by retraining with synthetic point clouds.

The phenomenon of crossover cooperation and convergence among services has gained increasing attention in the modern service industry. Service boundaries have been expansively stretched into other domains rather than limited to their original domains to achieve value creation, fostering the emergence of crossover services. Consequently, a complex service ecosystem takes shape. However, there is a lack of the convergence-evolution mechanism of crossover services for the adaptive transformation of service providers' businesses in this context. To address this problem, this paper proposes population-based and community-based convergence-evolution patterns from the ecological perspective. Based on the analysis of these evolution patterns and the driven force of service evolution, we propose an ecology-oriented evolution analysis method. Furthermore, we devise an automated tool to support the evolution design of crossover service ecosystems. Case studies and evaluation experiments show the feasibility and effectiveness of our proposed method and the corresponding tool.

Industry 4.0 changes traditional manufacturing relationships from isolated optimized cells to fully integrated data and product flows across borders with its technological pillars. However, the transition to Industry 4.0 is not a straightforward journey in which organizations need assistance. A well-known approach that can be utilized during the early phases of the transition is to assess the capability of the organization. Maturity models are frequently used to improve capability. In this systematic literature review (SLR), we analyzed 22 maturity and readiness models based on 10 criteria: year, type, focus, structure, research methodology followed during the design of models, base frameworks, tool support, community support, objectivity, and extent of usage in practice. Our SLR provides a well-defined comparison for organizations to choose and apply available models. This SLR showed that (1) there is no widely accepted maturity/readiness model for Industry 4.0, as well as no international standard; (2) only a few models have received positive feedback from the industry, whereas most do not provide any practical usage information; and (3) the objectivity of the assessment method is controversial in most of the models. We have also identified a number of issues as open research areas for assessing readiness and maturity models for Industry 4.0.

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 open-source software (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.

Graphical user interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, exploratory testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience-based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the diversity of testers' performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re-executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step toward filling this gap in the literature, in this work, we conduct an empirical evaluation involving four open-source Android apps and 20 masters students that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a capture and replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage that different-sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions.

Open source software (OSS) follows a software development paradigm whereby self-motivated volunteers scattered all around the globe contribute to the development in the form of code, documentation, feedback, feature recommendations, bug reporting, and bug resolution. These volunteers, commonly referred to as OSS project community, serve as the foundation of the OSS project, fostering its creation and sustenance and providing long-term support. The quality and sustainability of the OSS project is reliant upon the development and structure of the self-governing community. When a business organization plans to acquire an OSS solution, it not only takes into consideration the factors such as reliability, security, and scalability but also attaches significant importance to the likelihood of the OSS project being maintained and supported in the future so that it can rely on it as a stable and secure technology solution. Modern cloud-based software hosting platforms, such as GitHub, offer a range of options for automatically and freely accessing the complete development history of millions of OSS projects. This easy availability of detailed development history has enabled researchers to analyze and draw quantitative and scientific inferences about the quality of an OSS project which generally involves assessing three aspects, namely, software product, development process, and project community. With focus on project community part, a Framework for Assessment and Ranking of OSS Community is being presented in the current research work, following a detailed examination of the largest source code hosting and project collaboration platform, GitHub. Technique for Order of Preference by Similarity to Ideal Solution from Multi-Criteria Decision-Making toolkit has been utilized for assessing the quality of the project community. The framework has been validated by applying it on nine OSS projects and comparing the results with the outcomes obtained through an existing OSS evaluation methodology. The comparative analysis demonstrated that the proposed framework aligns with the aforementioned evaluation methodology while offering an opportunity for in-depth analysis on the dynamics of volunteer communities, which is lacking in previous evaluation methods. These insights can prove valuable for both potential adopters and project maintainers, aiding them in making informed strategic decisions.

Blockchain is a cross-cutting technology allowing interactions among untrusted entities in a distributed manner without the need for involving a trusted third party. Smart contracts (i.e., programs running on the blockchain) enabled organizations to envision and implement solutions to real-world problems in less cost and time. Given the immutability of blockchain and the lack of best practices for properly designing and developing smart contracts, it is crucial to assure smart contract quality before deployment. With the help of an exploratory survey involving developers and researchers, this paper identifies the practices and tools used to develop, implement, and evaluate smart contracts. The survey received 55 valid responses. Such responses indicate that (i) inefficiencies may occur during the development cycle of a smart contract, especially regarding requirements specification, design, and testing phases, and (ii) the lack of a shared standard to evaluate the functional quality of implemented smart contracts. To start coping with these issues, the adoption of functional suitability assessment measures recommended by the ISO/IEC 25000 standard, widely used in software engineering, is proposed by adapting them to the context of smart contracts. Through some examples, the manuscript also illustrates how to measure the functional completeness and correctness of smart contracts. The proposed procedure to measure smart contract functional suitability brings advantages to both developers and users of decentralized finance or non-fungible tokens platforms, data marketplaces, or shipping and real estate services, just to mention a few. In particular, it helps (i) better outline the responsibilities of smart contracts, (ii) uncover errors and deficiencies of smart contracts in the early stages, and (iii) ensure that the established requirements are met.

Design patterns represent a means of communicating reusable solutions to common problems, provided they are implemented and maintained correctly. However, many design pattern instances erode as they age, sacrificing qualities they once provided. Identifying such instances of pattern decay is valuable because it allows for proactive attempts to extend the longevity and quality attributes of pattern components. Apart from structural decay, design patterns can exhibit symptoms of behavioral decay. We utilized a taxonomy that characterizes these negative behaviors and designed a case study wherein we measured structural and behavioral decay, hereafter referred to as pattern grime, as well as pattern quality and size, across pattern evolutions. We evaluated the relationships between structural and behavioral grime and found statistically significant cases of strong correlations between specific types of structural and behavioral grime. Furthermore, we extended the QATCH operational software quality model to incorporate design pattern evolution metrics and measured and correlated software quality to the presence of behavioral grime in software systems. Our results suggest a strong inverse relationship between software quality and behavioral grime.