Journal of Systems and Software最新文献

Context:

The code review process is conducted by software teams with various motivations. Among other goals, code reviews act as a gatekeeper for software quality.

Objective:

In this study, we explore whether code reviews have an impact on one specific aspect of software quality, software maintainability. We further extend our investigation by analyzing whether code review process quality (as evidenced by the presence of code review process smells) influences software maintainability (as evidenced by the presence of code smells).

Method:

We investigate whether smells in the code review process are related to smells in the code that was reviewed by using correlation analysis. We augment our quantitative analysis with a focus group study to learn practitioners’ opinions.

Results:

Our investigations revealed that the level of code smells neither increases nor decreases in 8 out of 10 code reviews, regardless of the quality of the code review. Contrary to our own intuition and that of the practitioners in our focus groups, we found that code review process smells have little to no correlation with the level of code smells. We identified multiple potential reasons behind the counter-intuitive results based on our focus group data. Furthermore, practitioners still believe that code reviews are helpful in improving software maintainability.

Conclusion:

Our results imply that the community should update our goals for code review practices and reevaluate those practices to align them with more relevant and modern realities.

Static binary lifting is essential in binary rewriting frameworks. Existing tools overlook the impact of External Function Completion (EXFC) in static binary lifting. EXFC recovers the declarations of External Functions (EXFs, functions defined in standard shared libraries) using only the function symbols available. Incorrect EXFC can misinterpret the source binary, or cause memory overflows in static binary translation, which eventually results in program crashes. Notably, existing tools struggle to recover the declarations of mangled EXFs originating from binaries compiled from C++. Moreover, they require time-consuming manual processing to support new libraries.

This paper presents EFACT, an External Function Auto-Completion Tool for static binary lifting. Our EXF recovery algorithm better recovers the declarations of mangled EXFs, particularly addressing the template specialization mechanism in C++. EFACT is designed as a lightweight plugin to strengthen other static binary rewriting frameworks in EXFC. Our evaluation shows that EFACT outperforms RetDec and McSema in mangled EXF recovery by 96.4% and 97.3% on SPECrate 2017.

Furthermore, we delve deeper into static binary translation and address several cross-ISA EXFC problems. When integrated with McSema, EFACT correctly translates 36.7% more benchmarks from x86-64 to x86-64 and 93.6% more from x86-64 to AArch64 than McSema alone on EEMBC.

The digitization of the healthcare domain has the potential to drastically improve healthcare services, reduce the time to diagnosis, and lower costs. However, digital applications for the healthcare domain need to be interoperable to maximize their potential. Additionally, with the rapid expansion of Artificial Intelligence (AI) and, specifically, Machine Learning (ML), large amounts of diverse types of data are being utilized. Thus, to achieve interoperability in such applications, the adoption of common semantic data models becomes imperative. In this paper, we describe the adoption of such a common semantic data model, using the well-known Health Level Seven Fast Health Interoperability Resources (HL7 FHIR) standard, in a platform that assists in the creation and storage of a plethora of AI-based applications for several medical conditions. The FHIR server’s efficiency is being showcased by using it in an application predicting coronary artery stenosis as well as for managing the platform’s key performance indicators.

Efficient arithmetic operations in quantum circuits play a vital role in the implementation of quantum algorithms. Quantum circuits constructed exclusively using gates of the Clifford+T group are compatible with error detection and correction codes available in the quantum literature. However, the T gate, a member of this group, has a higher cost compared to other gates, making it crucial to minimize its usage to reduce circuit expenses. While the T gate cannot be entirely avoided since the Clifford group is not a universal set of gates, circuit optimization can effectively reduce the number of T gates required for implementation. In this work, we present a novel divider circuit for quantum computing that focuses on reducing the number of T gates while maintaining a reasonable number of qubits for this type of operation. To achieve this, we introduce variants of minor circuits, including a comparator and two types of subtractors. These circuits are based on published literature but undergo modifications to optimize their resource utilization for performing the division operation. The obtained results demonstrate that the proposed divider circuit outperforms other currently published divider circuits in terms of T gate usage, highlighting its efficiency and potential practicality in quantum algorithms.

Ensuring that all classes of objects are detected with equal accuracy is essential in AI systems. For instance, being unable to identify any one class of objects could have fatal consequences in autonomous driving systems. Hence, ensuring the reliability of image recognition systems is crucial. This work addresses how to validate group fairness in image recognition software. We propose a distribution-aware fairness testing approach (called DISTROFAIR) that systematically exposes class-level fairness violations in image classifiers via a synergistic combination of out-of-distribution (OOD) testing and semantic-preserving image mutation. DISTROFAIR automatically learns the distribution (e.g., number/orientation) of objects in a set of images. Then it systematically mutates objects in the images to become OOD using three semantic-preserving image mutations – object deletion, object insertion and object rotation. We evaluate DISTROFAIR using two well-known datasets (CityScapes and MS-COCO) and three major, commercial image recognition software (namely, Amazon Rekognition, Google Cloud Vision and Azure Computer Vision). Results show that about 21% of images generated by DISTROFAIR reveal class-level fairness violations using either ground truth or metamorphic oracles. DISTROFAIR is up to 2.3× more effective than two main baselines, i.e., (a) an approach which focuses on generating images only within the distribution (ID) and (b) fairness analysis using only the original image dataset. We further observed that DISTROFAIR is efficient, it generates 460 images per hour, on average. Finally, we evaluate the semantic validity of our approach via a user study with 81 participants, using 30 real images and 30 corresponding mutated images generated by DISTROFAIR. We found that images generated by DISTROFAIR are 80% as realistic as real-world images.

Code comments are crucial for program comprehension and maintenance. To better understand the nature and content of comments, previous work proposed taxonomies of comment information for textual languages, notably classical programming languages. However, paradigms such as model-driven or model-based engineering often promote the use of visual languages, to which existing taxonomies are not directly applicable. Taking MATLAB/Simulink as a representative of a sophisticated and widely used modeling environment, we extend a multi-language comment taxonomy onto new (visual) comment types and two new languages: Simulink and MATLAB. Furthermore, we outline Simulink commenting practices and compare them to textual languages. We analyze 259,267 comments from 9095 Simulink models and 17,792 MATLAB scripts. We identify the comment types, their usage frequency, classify comment information, and analyze their correlations with model metrics. We manually analyze 757 comments to extend the taxonomy. We also analyze commenting guidelines and developer adherence to them. Our extended taxonomy, SCoT (Simulink Comment Taxonomy), contains 25 categories. We find that Simulink comments, although often duplicated, are used at all model hierarchy levels. Of all comment types, Annotations are used most often; Notes scarcely. Our results indicate that Simulink developers, instead of extending comments, add new ones, and rarely follow commenting guidelines. Overall, we find Simulink comment information comparable to textual languages, which highlights commenting practice similarity across languages.

The gulf between industry and academic interests in Software Engineering appears to continue to widen. With software taking on an ever-growing role in society, it is somewhat concerning that the interests and research focuses of academic software engineers seem to rarely overlap with industry interests. In this editorial, I hypothesize four major concerns I have with the applicability of much of the research in this area. I also surface the important meta question: What is Software Engineering Research? Is this the study of software engineers, or the study of best practices in software engineering? Does academic SE research owe more to anthropology, or the study of what makes software successful?

Context:

Mobile applications are growing in prevalence. Cross-platform application is one of the main challenges within mobile application development. Aiming to facilitate the process of mobile application development, the software engineering community developed multiple solutions to support cross-platform application development. Flutter is a recent cross-platform software development kit (SDK) that has been rising in popularity.

Objective:

This study aims to gain a better understanding of Flutter stance on Stack Overflow (SO).

Method:

The study identified and analyzed 176,876 Flutter-related questions to understand the interest towards Flutter. The study utilized Latent Dirichlet Allocation (LDA) to identify Flutter-related topics discussed within the identified questions. Subsequently, a number of heuristics were utilized to gauge the popularity and difficulty of topics.

Results:

The study revealed that interest towards Flutter was steadily increasing on SO until it dropped in 2023 and SO users discuss 12 Flutter-related topics, with the topic of setup and build automation being the most popular and most difficult.

Conclusion:

This study highlights the interest towards Flutter on SO, identifies Flutter-related topics discussed on the website, and assess the topics popularity and difficulty. Software researchers, practitioners, educators, and Flutter contributors may utilize the results to steer their future Flutter-related efforts.

Given the abundance of software in open source repositories, code search engines are increasingly turning to “big data” technologies such as natural language processing and machine learning, to deliver more useful search results. However, like the syntax-based approaches traditionally used to analyze and compare code in the first generation of code search engines, big data technologies are essentially static analysis processes. When dynamic properties of software, such as run-time behavior (i.e., semantics) and performance, are among the search criteria, the exclusive use of static algorithms has a significant negative impact on the precision and recall of the search results as well as other key usability factors such as ranking quality. Therefore, to address these weaknesses and provide a more reliable and usable service, the next generation of code search engines needs to complement static code analysis techniques with equally large-scale, dynamic analysis techniques based on its execution and observation. In this paper we describe a new software platform specifically developed to achieve this by simplifying and largely automating the dynamic analysis (i.e., observation) of code at a large scale. We show how this platform can combine dynamically observed properties of code modules with static properties to improve the quality and usability of code search results.

Quantum computers must meet extremely stringent qualitative and quantitative requirements on their qubits in order to solve real-life problems. Quantum circuit fragmentation techniques divide a large quantum circuit into a number of sub-circuits that can be executed on the smaller noisy quantum hardware available. However, the process of quantum circuit fragmentation involves finding an ideal cut that has exponential time complexity and also the classical post-processing required to reconstruct the output. In this paper, we represent a quantum circuit using a weighted graph and propose a novel classical graph partitioning algorithm for selecting an efficient fragmentation that reduces the entanglement between the sub-circuits along with balancing the estimated error in each sub-circuit. We also demonstrate a comparative study of different classical and quantum approaches to graph partitioning for finding such a cut. We present FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold. With this proposed approach, we achieve an increase in fidelity of 13.38% compared to direct execution without cutting the circuit, and 7.88% over the state-of-the-art ILP-based method for the benchmark circuits.

The code for FragQC is available at https://github.com/arnavdas88/FragQC.