Automated Software Engineering最新文献

Machine Learning (ML) is integrated into a growing number of systems for various applications. Because the performance of an ML model is highly dependent on the quality of the data it has been trained on, there is a growing interest in approaches to detect and repair data errors (i.e., data cleaning). Researchers are also exploring how ML can be used for data cleaning; hence creating a dual relationship between ML and data cleaning. To the best of our knowledge, there is no study that comprehensively reviews this relationship. This paper’s objectives are twofold. First, it aims to summarize the latest approaches for data cleaning for ML and ML for data cleaning. Second, it provides future work recommendations. We conduct a systematic literature review of the papers published between 2016 and 2022 inclusively. We identify different types of data cleaning activities with and for ML: feature cleaning, label cleaning, entity matching, outlier detection, imputation, and holistic data cleaning. We summarize the content of 101 papers covering various data cleaning activities and provide 24 future work recommendations. Our review highlights many promising data cleaning techniques that can be further extended. We believe that our review of the literature will help the community develop better approaches to clean data.

Developing embedded software applications is a challenging task, chiefly due to the limitations that are imposed by the hardware devices or platforms on which they operate, as well as due to the heterogeneous non-functional requirements that they need to exhibit. Modern embedded systems need to be energy efficient and dependable, whereas their maintenance costs should be minimized, in order to ensure the success and longevity of their application. Being able to build embedded software that satisfies the imposed hardware limitations, while maintaining high quality with respect to critical non-functional requirements is a difficult task that requires proper assistance. To this end, in the present paper, we present the SDK4ED Platform, which facilitates the development of embedded software that exhibits high quality with respect to important quality attributes, with a main focus on energy consumption, dependability, and maintainability. This is achieved through the provision of state-of-the-art and novel quality attribute-specific monitoring and optimization mechanisms, as well as through a novel fuzzy multi-criteria decision-making mechanism for facilitating the selection of code refactorings, which is based on trade-off analysis among the three main attributes of choice. Novel forecasting techniques are also proposed to further support decision making during the development of embedded software. The usefulness, practicality, and industrial relevance of the SDK4ED platform were evaluated in a real-world setting, through three use cases on actual commercial embedded software applications stemming from the airborne, automotive, and healthcare domains, as well as through an industrial study. To the best of our knowledge, this is the first quality analysis platform that focuses on multiple quality criteria, which also takes into account their trade-offs to facilitate code refactoring selection.

Automated test data generation for unit testing C/C++ functions using concolic testing has been known for improving software quality while reducing human testing effort. However, concolic testing could face challenging problems when tackling complex practical projects. This paper proposes a concolic-based method named Automated Unit Testing and Stubbing (AUTS) for automated test data and stub generation. The key idea of the proposed method is to apply the concolic testing approach with three major improvements. Firstly, the test data generation, which includes two path search strategies, not only is able to avoid infeasible paths but also achieves higher code coverage. Secondly, AUTS generates appropriate values for specialized data types to cover more test scenarios. Finally, the proposed method integrates automatic stub preparation and generation to reduce the costs of human effort. The method even works on incomplete source code or missing libraries. AUTS is implemented in a tool to test various C/C++ industrial and open-source projects. The experimental results show that the proposed method significantly improves the coverage of the generated test data in comparison with other existing methods.

Regression testing is an important activity that aims to provide information about the quality of the software product under test when changes occur. The two primary techniques for optimizing regression testing are test case selection and prioritization. To identify features affected by a change and determine the best test cases for selection and prioritization, techniques allowing the semantic representation and the quantification of testing concepts are required. The goal of this paper is threefold. Firstly, we proposed an ontology-based test case selection model that enables automated regression testing by dynamically selecting appropriate test cases. The selection of test cases is based on a semantic mapping between change requests and their associated test suites and test cases. Secondly, the selected test cases are prioritized based on their functional size. The functional size is determined using the COmmon Software Measurement International Consortium (COSMIC) Functional Size Measurement (FSM) method. The test case prioritization attempts to reorganize test case execution in accordance with its goal. One common goal is fault detection, in which test cases with a higher functional size (i.e., with a higher chance of detecting a fault) are run first, followed by the remaining test cases. Thirdly, we built an automated testing tool using the output of the aforementioned processes to validate the robustness of our proposed research methodology. Results from a case study in the automotive industry domain show that semantically presenting change requests and using standardized FSM methods to quantify their related test cases are the most interesting metrics. Obviously, they assist in the automation of regression testing and, therefore, in all the software testing processes.

Reusing APIs can greatly expedite the software development process and reduce programming effort. To learn how to use APIs, developers often rely on API learning resources (such as API references and tutorials) that contain rich and valuable API knowledge. In recent years, numerous API analytic approaches have been presented to help developers mine API knowledge from API learning resources. While these approaches have shown promising results in various tasks, there are many opportunities in this area. In this paper, we discuss several possible future works on API analytics.

This paper introduces ALICE (Automated Logic for Identifying Contradictions in Engineering), a novel automated contradiction detection system tailored for formal requirements expressed in controlled natural language. By integrating formal logic with advanced large language models (LLMs), ALICE represents a significant leap forward in identifying and classifying contradictions within requirements documents. Our methodology, grounded on an expanded taxonomy of contradictions, employs a decision tree model addressing seven critical questions to ascertain the presence and type of contradictions. A pivotal achievement of our research is demonstrated through a comparative study, where ALICE’s performance markedly surpasses that of an LLM-only approach by detecting 60% of all contradictions. ALICE achieves a higher accuracy and recall rate, showcasing its efficacy in processing real-world, complex requirement datasets. Furthermore, the successful application of ALICE to real-world datasets validates its practical applicability and scalability. This work not only advances the automated detection of contradictions in formal requirements but also sets a precedent for the application of AI in enhancing reasoning systems within product development. We advocate for ALICE’s scalability and adaptability, presenting it as a cornerstone for future endeavors in model customization and dataset labeling, thereby contributing a substantial foundation to requirements engineering.

Software design optimization (SDO) demands advanced abstract reasoning to define optimal design components’ structure and interactions. Modeling tools such as UML and MERISE, and to a degree, programming languages, are chiefly developed for lucid human–machine design dialogue. For effective automation of SDO, an abstract layer attuned to the machine’s computational prowess is crucial, allowing it to harness its swift calculation and inference in determining the best design. This paper contributes an innovative and universal framework for search-based software design refactoring with an emphasis on optimization. The framework accommodates 44% of Fowler’s cataloged refactorings. Owing to its adaptable and succinct structure, it integrates effortlessly with diverse optimization heuristics, eliminating the requirement for further adaptation. Distinctively, our framework offers an artifact representation that obviates the necessity for a separate solution representation, this unified dual-purpose representation not only streamlines the optimization process but also facilitates the computation of essential object-oriented metrics. This ensures a robust assessment of the optimized model through the construction of pertinent fitness functions. Moreover, the artifact representation supports parallel optimization processes and demonstrates commendable scalability with design expansion.

Success in software projects is now an important challenge. The main focus of the engineering community is to predict software defects based on the history of classes and other code elements. However, these software defect prediction techniques are effective only as long as there is enough data to train the prediction model. To mitigate this problem, cross-project defect prediction is used. The purpose of this research investigation is twofold: first, to replicate the experiments in the original paper proposal, and second, to investigate other settings regarding defect prediction with the aim of providing new insights and results regarding the best approach. In this study, three composite algorithms, namely AvgVoting, MaxVoting and Bagging are used. These algorithms integrate multiple machine classifiers to improve cross-project defect prediction. The experiments use pre-processed methods (normalization and standardization) and also feature selection. The results of the replicated experiments confirm the original findings when using raw data for all three methods. When normalization is applied, better results than in the original paper are obtained. Even better results are obtained when feature selection is used. In the original paper, the MaxVoting approach shows the best performance in terms of the F-measure, and BaggingJ48 shows the best performance in terms of cost-effectiveness. The same results in terms of F-measure were obtained in the current experiments: best MaxVoting, followed by AvgVoting and then by BaggingJ48. Our results emphasize the previously obtained outcome; the original study is confirmed when using raw data. Moreover, we obtained better results when using preprocessing and feature selection.

In the domain of cloud computing and distributed systems, microservices architecture has become preeminent due to its scalability and flexibility. However, the distributed nature of microservices systems introduces significant challenges in maintaining operational reliability, especially in fault localization. Traditional methods for fault localization are insufficient due to time-intensive and prone to error. Addressing this gap, we present SpanGraph, a novel framework employing graph convolutional networks (GCN) to achieve efficient span-level fault localization. SpanGraph constructs a directed graph from system traces to capture invocation relationships and execution times. It then utilizes GCN for edge representation learning to detect anomalies. Experimental results demonstrate that SpanGraph outperforms all baseline approaches on both the Sockshop and TrainTicket datasets. We also conduct incremental experiments on SpanGraph using unseen traces to validate its generalizability and scalability. Furthermore, we perform an ablation study, sensitivity analysis, and complexity analysis for SpanGraph to further verify its robustness, effectiveness, and flexibility. Finally, we validate SpanGraph’s effectiveness in anomaly detection and fault location using real-world datasets.

The Inter-Component Communication (ICC) model in Android enables the sharing of data and services among app components. However, it has been associated with several problems, including complexity, support for unconstrained communication, and difficulties for developers to understand. These issues have led to numerous security vulnerabilities in Android ICC. While existing research has focused on specific subsets of these vulnerabilities, it lacks comprehensive and scalable modeling of app specifications and interactions, which limits the precision of analysis. To tackle these problems, we introduce VAnDroid3, a Model-Driven Reverse Engineering (MDRE) framework. VAnDroid3 utilizes purposeful model-based representations to enhance the comprehension of apps and their interactions. We have made significant extensions to our previous work, which include the identification of six prominent ICC vulnerabilities and the consideration of both Intent and Data sharing mechanisms that facilitate ICCs. By employing MDRE techniques to create more efficient and accurate domain-specific models from apps, VAnDroid3 enables the analysis of ICC vulnerabilities on intra- and inter-app communication levels. We have implemented VAnDroid3 as an Eclipse-based tool and conducted extensive experiments to evaluate its correctness, scalability, and run-time performance. Additionally, we compared VAnDroid3 with state-of-the-art tools. The results substantiate VAnDroid3 as a promising framework for revealing Android inter-app ICC security issues.