Diabetes mellitus is a chronic metabolic disorder that necessitates frequent blood glucose monitoring, usually through painful and inconvenient methods. Volatile organic compounds (VOCs) in breath have been used as biomarkers for diabetes detection in non-invasive, Internet of Things (IoT)-based devices. Nevertheless, the cost, compactness, and mobility challenges of existing devices limit their general adoption. We present DiaBreath, a novel, affordable, non-invasive multi-sensor device for the early prediction of diabetes, solving these challenges. DiaBreath consists of (a) a breath analyzer containing MOS-based sensors, optimally selected via an ablation study to capture VOC responses (b) a feature engineering pipeline to to extract feature set, (c) a machine-learning model for reliable diabetes prediction, and (d) a simple user interface that generates prediagnostic diabetes reports. DiaBreath exhibits superior predictive power, with an accuracy of 97.6%, to enable efficient and scalable early diagnosis in public health centers, especially in resource-constrained settings. DiaBreath’s low cost and compact size make it highly adaptable for implementation in rural and underserved regions, where access to timely diabetes screening is limited. This technology improves non-invasive diabetes monitoring, making early diagnosis more cost-effective and accessible globally.

Software development effort estimation (SDEE) generally involves leveraging the information about the effort spent in developing similar software in the past. Most organizations do not have access to sufficient and reliable forms of such data from past projects. As such, the existing SDEE methods suffer from low usage and accuracy. We propose an efficient SDEE method for open source software, which provides accurate and fast effort estimates. The significant contributions of our article are (i) novel SDEE software metrics derived from developer activity information of various software repositories, (ii) an SDEE dataset comprising the SDEE metrics’ values derived from approximately 13,000 GitHub repositories from 150 different software categories, and (iii) an effort estimation tool based on SDEE metrics and a software description similarity model . Our software description similarity model is basically a machine learning model trained using the PVA on the software product descriptions of GitHub repositories. Given the software description of a newly envisioned software, our tool yields an effort estimate for developing it. Our method achieves the highest standardized accuracy score of 87.26% (with Cliff’s δ = 0.88 at 99.999% confidence level) and 42.7% with the automatically transformed linear baseline model. Our software artifacts are available at https://doi.org/10.5281/zenodo.5095723.

Practical quantum computing is rapidly becoming a reality. To harness quantum computers’ real potential in software applications, one needs to have an in-depth understanding of all such characteristics of quantum computing platforms (QCPs), relevant from the Software Engineering (SE) perspective. Restrictions on copying, deletion, the transmission of qubit states, a hard dependency on quantum algorithms are few, out of many, examples of QCP characteristics that have significant implications for building quantum software.Thus, developing quantum software requires a paradigm shift in thinking by software engineers. This paper presents the key findings from the SE perspective, resulting from an in-depth examination of state-of-the-art QCPs available today. The main contributions that we present include i) Proposing a general architecture of the QCPs, ii) Proposing a programming model for developing quantum software, iii) Determining architecturally significant characteristics of QCPs, and iv) Determining the impact of these characteristics on various Quality Attributes (QAs) and Software Development Life Cycle (SDLC) activities.We show that the nature of QCPs makes them useful mainly in specialized application areas such as scientific computing. Except for performance and scalability, most of the other QAs (e.g., maintainability, testability, and reliability) are adversely affected by different characteristics of a QCP.

An important issue faced during software development is to identify defects and the properties of those defects, if found, in a given source file. Determiningdefectivenessof source code assumes significance due to its implications on software development and maintenance cost. We present a novel system to estimate the presence of defects in source code and detect attributes of the possible defects, such as the severity of defects. The salient elements of our system are: (i) a dataset of newly introduced source code metrics, calledPROgrammingCONstruct (PROCON) metrics, and (ii) a novelMachine-Learning (ML)-based system, calledDefectEstimator forSourceCode (DESCo), that makes use of PROCON dataset for predicting defectiveness in a given scenario. The dataset was created by processing 30,400+ source files written in four popular programming languages, viz., C, C++, Java, and Python. The results of our experiments show that DESCo system outperforms one of the state-of-the-art methods with an improvement of 44.9%. To verify the correctness of our system, we compared the performance of 12 different ML algorithms with 50+ different combinations of their key parameters. Our system achieves the best results with SVM technique with a mean accuracy measure of 80.8%.

Cost of a skilled and competent software developer is high, and it is desirable to minimize dependency on such costly human resources. One of the ways to minimize such costs is via automation of various software development tasks. Recent advances in Artificial Intelligence (AI) and the availability of a large volume of knowledge bearing data at various software development related venues present a ripe opportunity for building tools that can automate software development tasks. For instance, there is significant latent knowledge present in raw or unstructured data associated with items such as source files, code commit logs, defect reports, comments, and so on, available in the Open Source Software (OSS) repositories. We aim to leverage such knowledge-bearing data, the latest advances in AI and hardware to create knowledge warehouses and expert systems for the software development domain. Such tools can help in building applications for performing various software development tasks such as defect prediction, effort estimation, code review, etc.