Journal of Software-Evolution and Process最新文献

The relationship between test code and production code, that is, test-to-code traceability, plays an essential role in the verification, reliability, and certification of software systems. Prior work on test-to-code traceability focuses mainly on Java. However, as Python allows more flexible testing styles, it is still unknown whether existing traceability approaches work well on Python projects. In order to address this gap in knowledge, this paper evaluates whether existing traceability approaches can accurately identify test-to-code links in Python projects. We collected seven popular Python projects and carried out an exploratory study at both the method and module levels (involving a total of 3198 test cases). On these projects, we evaluated 15 individual traceability techniques along with cross-level information propagation and four combining resolution strategies. The results reveal that the performance of test-to-code traceability approaches on Python has many differences with Java: (1) most of the existing techniques have poor effectiveness for Python; (2) after augmenting with cross-level information, the recall surprisingly drops; and (3) machine learning based combination approach achieves the best recall but the worst precision. These findings shed light on the best traceability approaches for Python projects, and also provide guidelines for researchers and the Python community.

Transformer methods have revolutionized software requirements classification by combining advanced natural language processing to accurately understand and categorize requirements. While traditional methods like Doc2Vec and TF-IDF are useful, they often fail to capture the deep contextual relationships and subtle meanings inherent in textual data. Transformer models possess unique strengths and weaknesses, impacting their ability to capture various aspects of the data. Consequently, relying on a single model can lead to suboptimal feature representations, limiting the overall performance of the classification task. To address this challenge, our study introduces an innovative BET-BiLSTM (balanced ensemble transformers using Bi-LSTM) model. This model combines the strengths of five transformer–based models BERT, RoBERTa, XLNet, GPT-2, and T5 through weighted averaging ensemble, resulting in a sophisticated and resilient feature set. By employing data balancing techniques, we ensure a well-distributed representation of features, addressing the issue of class imbalance. The BET-BiLSTM model plays a crucial role in the classification process, achieving an impressive accuracy of 96%. Moreover, the practical applicability of this model is validated through its successful implementation on three publicly available unlabeled datasets and one additional labeled dataset. The model significantly improved the completeness and reliability of these datasets by accurately predicting labels for previously unclassified requirements. This makes our approach a powerful tool for large-scale requirements analysis and classification tasks, outperforming traditional single-model methods and showcasing its real-world effectiveness.

The GitHub issue community serves as the primary means for project developers to obtain information about program bugs, and numerous GitHub users post issues based on encountered project vulnerabilities or error messages. However, these issues often vary in quality, leading to a significant time burden on project developers. By collecting 2500 bug-related issues from five GitHub projects, we first manually analyze a large volume of issue information to formulate rules for identifying whether a bug-tagged issue is truly fixed by project developers. We find that a substantial number (ranging from 29% to 68.4% in different projects) of bug-tagged issues are not truly fixed by project developers. We empirically investigate the characteristics of such issues and summarize the reasons why they are not fixed. Then, we propose an automated approach called DFBERT to identify the bug-tagged issues that are more likely to be fixed by project developers. Our approach incorporates both text and non-text features to train a neural network-based prediction model. The experimental results show that our approach achieves an average F1-score of 0.66 in inter-project setting, and the F1-score increase to 0.77 when adding part of testing data for training.

GUI ripping explores the graphical user interface of an application to build a model which can express the application behavior. The ripped GUI model is useful in various software engineering tasks. Traditional GUI ripping techniques depend on the underlying GUI frameworks to provide the GUI structure information. They are difficult to work across platforms or on nonnative applications where the GUI structure information cannot easily be obtained. This work introduces cvrip, a visual GUI ripping framework, to address the problem. cvrip visually analyzes the GUI screen for ripping and does not rely on the underlying GUI frameworks. We introduce many new techniques to enable efficient visual GUI ripping, for example, a YOLO v5-based model to detect executable widgets, a state recognition acceleration method for fast model updating, and several GUI exploration strategies taking the characteristics of imperfect visual analysis into account. Experiments are conducted to evaluate many technique choices in visual GUI ripping and compare the solution with the traditional style ripping. The results show that cvrip can get competitive exploration coverage compared to traditional approaches. This suggests visual GUI ripping is a direction worthy of more future studies.

Organizations use key performance indicators (KPIs) to assess the effectiveness and efficiency of their procedures and processes. In a world that is constantly evolving and hyperconnected via the internet, it is of great interest to analyze how changes (organizational, legal, technological or other) can lead to modifications in the KPIs involved. However, little attention has been paid to KPI evolution either in the scientific literature or in developed solutions. This paper presents A Flexible Framework for the Evolution, Consistency and Traceability of KPIs (AFFECTK) that aims at establishing the basis for suitable KPIs' evolution management. The feasibility of this proposal is demonstrated through a proof-of-concept developed using a reasoning tool based on Constraint Logic Programming. The framework is further evaluated, using real KPI case studies, to assess the functional suitability of our approach.

Code comprehension is often supported by source code analysis tools that provide more abstract views over software systems, such as those detecting design patterns. These tools encompass analysis of source code and ensuing extraction of relevant information. However, the analysis of the source code is often specific to the target programming language. We propose DP-LARA, a multilanguage pattern detection tool that uses the multilanguage capability of the LARA framework to support finding pattern instances in a code base. LARA provides a virtual AST, which is common to multiple OOP programming languages, and DP-LARA then performs code analysis of detecting pattern instances on this abstract representation. We evaluate the detection performance and consistency of DP-LARA with a few software projects. Results show that a multilanguage approach does not compromise detection performance, and DP-LARA is consistent across the languages we tested it for (i.e., Java and C/C++). Moreover, by providing a virtual AST as the abstract representation, we believe to have decreased the effort of extending the tool to new programming languages and maintaining existing ones.

The evolution (e.g., development and maintenance) of deep learning (DL) models has attracted much attention. One of the main challenges during the development and maintenance of DL models is model training, which often requires a lot of human resources and computing power (such as labeling costs and parameter training). In recent years, to alleviate this problem, researchers have introduced the idea of software engineering (SE) into DL. Researchers consider the DL model a new type of software, borrowing the practice of traditional software reuse, that is, focusing on the reuse of DL models to improve the quality of DL model development and maintenance. This paper focuses on more complex model reuse scenarios, where developers need to combine multiple models with functional overlaps. We explore whether the model combination technique can meet the requirements for such scenarios. We have conducted an empirical study of the research scenario and found that a model composition approach was needed to meet the requirements. Furthermore, we propose a model combination method based on concatenation-parallel called MCCP. First, the multiple models' hidden layer features are connected, and then the multiple models are connected in parallel to construct a joint model with all output categories. The joint model is trained to achieve unified requirements under the limited marking cost. Through experiments on data sets in nine domains and five model structures, the following two conclusions are drawn: (1) we observe noticeable differences (38% at most) in the performance of multiple models within overlapping category data, which calls for effective model combination techniques. (2) MCCP is more effective than the baseline, which performs the best in eight of the nine domains. Our research shows that the joint model generated by combining models with overlapping functions can meet the requirements of complex model reuse scenarios.

As the size and the complexity of business process models are an important driver of error probability, it is recommended to split large models into smaller models. Hence, we propose, in this paper, to develop business process models by refinement. A refinement is a transformation of a source model to a target model expressed in the same modeling language. This transformation should preserve the semantics of the source model to provide semantically correct target model. Thus, we propose, in this paper, a domain specific language based on Business Process Model and Notation (BPMN) language for developing by refinement business process models correct-by-construction. Hence, we propose (i) a $��B�P�M�{�N�}_{�R�}�$ formal syntax throughout a context-free grammar $��{�G�}_{�B�P�M�{�N�}_{�R�}�}�$, (ii) axiomatic semantics to ensure the refinement correction when building business process models, (iii) operational semantics in terms of Kripke structure permitting formal verification of provided $��B�P�M�{�N�}_{�R�}�$ models to check their reliability. The Kripke structure supports the verification of behavioral requirements represented by the Computational Tree Logic (CTL) temporal logic and verified by NuSMV model checker. Based on these semantics, we prove the validity