Journal of Software-Evolution and Process最新文献

Smart contracts executed on blockchains are interactive programs where external actors generate events that trigger function invocations. Events can be emitted by participants asynchronously. However, some functionalities should be restricted to participants inhabiting specific roles in the system, which might be dynamically adjusted while the system evolves. We argue that current smart contract languages adopting imperative programming paradigms require additional complicated access control code. Furthermore, smart contracts are often developed and evolved independently and cannot share a joint access control policy. This makes it challenging to ensure the correctness of access control properties and to maintain correctness when the contracts are adapted. We propose using dynamic condition response (DCR) graphs for role-based and declarative access control for smart contracts and techniques for test-driven modelling and refinement of DCR graphs to support the safe design and evolution of smart contracts. We show that they allow for capturing and visualizing a form of dynamic access control where access rights evolve as the contract state progresses. Their use supports the straightforward declaration of access control rights, improved code auditing, test-driven modelling, and safe evolution of smart contracts and improves users' understanding.

In safety-critical systems engineering, regulations such as Automotive SPICE, ISO26262, or ED-109A mandate software quality assurance measures to provide evidence that the developed system is high quality. The constraints that define quality assurance conditions during the engineering life cycle are often non-trivial. This paper addresses the challenges, engineers face who are unfamiliar with the precise constraints of various projects (e.g., when newly joining a company or switching between departments). Understanding how to fulfill a constraint is a time-consuming and challenging task as an engineer needs to determine the most suitable option (out of potentially many) to fulfill a constraint violation. To this end, we propose a guidance action ranking framework to provide engineers with the most relevant guidance actions. Our primary ranking algorithm analyzes in the background the actions that engineers have made in the past to resolve a constraint violation without requiring explicit feedback from them. We evaluated our framework on two real-world data sets: an open-source drone management and an industrial air traffic control software system. Concretely, we replay past engineering activities and measured whether, in the case of a constraint violation, our suggested guidance actions were indeed selected by the engineer. The evaluation results revealed that learning from prior guidance actions effectively identifies the most appropriate guidance actions (ranked top 1 or 2) when compared to ranking algorithms based on action simplicity and artifact property change frequency. Specifically, we achieve a median MRR of 0.95 for the first case study and 0.94 for the second case study: an improvement of 80% and 100% over the baseline. Additionally, we observed that the simplicity of a guidance action does not reliably indicate its suitability for fulfilling a constraint, whereas learning from prior change operation property out-performed simplicity-based ranking but did not surpass guidance frequency-based ranking.

Maintaining software quality is a significant challenge as the complexity of software is increasing with the rise of the software industry. Software defects are a primary concern in complex modules, and predicting them in the early stages of the software development life cycle (SDLC) is difficult. Previous techniques to address this issue have not been very promising. We have proposed “A hybrid ensemble model for software defect prediction using AI-based techniques with feature preservation” to overcome this problem. We have used the National Aeronautics and Space Administration (NASA) dataset from the PROMISE repository for testing and validation. By applying exploratory data analysis (EDA), feature engineering, scaling, and standardization, we found that the dataset is imbalanced, which can negatively affect the model's performance. To address this, we have used the Synthetic Minority Oversampling (SMOTE) technique and the edited nearest neighbor (ENN) (SMOTE-ENN). We have also used recursive feature elimination cross-validation (RFE-CV) with a pipeline to prevent data leaking in CV and kernel-based principal component analysis (K-PCA) to minimize dimensionality and selectively relevant features. The reduced dimensional data is then given to the eXtreme Gradient Boosting (XGBoost) for classification, resulting in the hybrid-ensemble (SMERKP-XGB) model. The proposed SMERKP-XGB model is better than previously developed models in terms of accuracy (CM1: 97.53%, PC1: 92.05%, and PC2: 97.45%, KC1:95.65%), and area under the receiver operating characteristic curve values (CM1:96.30%, PC1:98.30%, and PC2:99.30%: KC1: 93.54) and other evaluation criteria mentioned in the literature.

Large language models (LLMs) have been touted to enable increased productivity in many areas of today's work life. Scientific research as an area of work is no exception: The potential of LLM-based tools to assist in the daily work of scientists has become a highly discussed topic across disciplines. However, we are only at the very onset of this subject of study. It is still unclear how the potential of LLMs will materialize in research practice. With this study, we give first empirical evidence on the use of LLMs in the research process. We have investigated a set of use cases for LLM-based tools in scientific research and conducted a first study to assess to which degree current tools are helpful. In this position paper, we report specifically on use cases related to software engineering, specifically, on generating application code and developing scripts for data analytics and visualization. While we studied seemingly simple use cases, results across tools differ significantly. Our results highlight the promise of LLM-based tools in general, yet we also observe various issues, particularly regarding the integrity of the output these tools provide.

Software engineers play a centric role throughout the software development lifecycle. Their activities directly impact the quality, performance, and successful delivery of software products, in particular for enterprises with an emphasis on high levels of quality assurance and timely delivery. Proper incentives that motivate software engineers are vital to secure and continuously improve development productivity and software quality. However, most existing research ignores the positive incentives for software engineers, especially industry-oriented research. In addition, existing research largely relies on peer assessment and lacks objectivity and transparency. To this end, this study investigates the process of contribution measurement for software engineers in a global Information and Communications Technology (ICT) enterprise, to explore the practical experiences and significance of contribution measurement. We investigated the practices of contribution measurement through multiple methods, including archival analysis, interviews, and survey. A total of 22 software engineers were interviewed to understand the practical implementation process of measuring contributions and its impact on software processes as well as engineers. In addition, 74 responses to our questionnaire were collected and used for a comprehensive impact analysis on software engineers. The analysis results reveal five benefits for software development processes and four benefits for practitioners of contribution measurement in the studied enterprise. In addition, this study reports on the best practices of contribution measurement, such as team-specific measurements, and provides a practical reference for researchers and organizations interested in studying or performing contribution measurement.

Atomicity violation bugs are a frequent problem in concurrency. Because of the unpredictable nature of thread interleaving, most current methods are unable to differentiate between harmful and benign atomicity violations. This makes it challenging to determine the existence of an actual bug. This paper presents a method for detecting atomicity violation bugs in programs based on user interaction. First, UserTrack matches access interleaving patterns to identify all potential violations. We then verify the programmer's atomicity intent between two logical access operations on the same thread and filter out some candidates. Finally, a user interaction mechanism checks the paths that do not produce atomicity violations when threads are interleaved. Our method focuses on a small number of interesting states and interleavings, while allowing the programmer to impose constraints on thread interleavings and explore all executions that satisfy these constraints. We continuously gather information through user feedback results and detect atomicity violation bugs that truly go against the programmer's intent. We evaluated the method using benchmark tests, which demonstrated the effectiveness of UserTrack in detecting atomicity violations.

End-to-end (E2E) testing is a commonly used technique for web application testing. Unlike traditional unit tests, E2E tests focus on test scenarios that target the entire business, which integrate and collaborate of various components and services to make the whole application work. As a result, one drawback of E2E tests is their longer execution time, seriously affecting testing efficiency of web applications. In order to speed up the execution of E2E tests for web applications, this paper proposes a test execution optimization approach based page state reuse. Through analyzing the common operations of E2E tests, a common prefix trees is constructed to organize the same operations among test scripts in a test suite of web applications. Under the guidance of the prefix tree, the optimal reusable state is identified and duplicated to maximize the utilization of page states triggered by the same operations, thereby reducing the overall execution time of the test suite. Besides, to realize page state reuse automatically, we design a browser process replication strategy, which implement querying the active page and duplicating the web page. To verify the effectiveness of our method, experiments and evaluations were conducted on 347 E2E tests from eight open-source web applications, and the results showed that our approach reduced the E2E testing execution time for web applications by 52%–68%.