Software Size Research Articles

Molecular characterization and polymorphism detection in HSPB6 gene in Sahiwal cattle

The present study was undertaken with the objectives of molecular characterization and detection of genetic polymorphisms in the HSPB6 gene of Sahiwal cattle. A total of 100 Sahiwal cattle maintained at LRC, NDRI, Karnal (Haryana) were included in the study. HSPB6 gene has been mapped on Bos taurus autosome 18 (BTA 18) and spans nearly 2620 bp comprising 3 exons and 2 introns. Four sets of forward and reverse gene-specific oligonucleotide primers of HSPB6 gene were designed using Primer3 software and amplicon size of 529 bp, 460 bp, 670 bp and 502 bp were ascertained. On the basis of comparative sequence analysis, total three polymorphic loci (SNPs) were detected at locus G436A, A2152G and A2417T in HSPB6 gene as compare to Bos taurus (NCBI GenBank AC_000175.1). Out of these three SNPs, two (A2152G and A2417T) were found in 3´-UTR region and third one (G436A) was detected in intronic region 1. However, monomorphic pattern was found in exon 2 of HSPB6 gene in Sahiwal cattle. Further research need be directed to find out the association of allelic variants in major heat shock protein genes to establish markers for the future genetic improvement of livestock through marker assisted selection.

Early Estimation in Agile Software Development Projects: A Systematic Mapping Study

Estimating during the early stages is crucial for determining the feasibility and conducting the budgeting and planning of agile software development (ASD) projects. However, due to the characteristics of ASD and limited initial information, these estimates are often complicated and inaccurate. This study aims to systematically map the literature to identify the most used estimation techniques; the reasons for their selection; the input artifacts, predictors, and metrics associated with these techniques; as well as research gaps in early-stage estimations in ASD. This study was based on the guidelines proposed by Kitchenham for systematic literature reviews in software engineering; a review protocol was defined with research questions and criteria for the selection of empirical studies. Results show that data-driven techniques are preferred to reduce biases and inconsistencies of expert-driven techniques. Most selected studies do not mention input artifacts, and software size is the most commonly used predictor. Machine learning-based techniques use publicly available data but often contain records of old projects from before the agile movement. The study highlights the need for tools supporting estimation activities and identifies key areas for future research, such as evaluating hybrid approaches and creating datasets of recent projects with sufficient contextual information and standardized metrics.

Extended Association Rule Mining and Its Application to Software Engineering Data Sets

Association rule mining is a highly effective approach to data analysis for datasets of varying sizes, accommodating diverse feature values. Nevertheless, deriving practical rules from datasets with numerical variables presents a challenge, as these variables must be discretized beforehand. Quantitative association rule mining addresses this issue, allowing the extraction of valuable rules. This paper introduces an extension to quantitative association rules, incorporating a two-variable function in their consequent part. The use of correlation functions, statistical test functions, and error functions is also introduced. We illustrate the utility of this extension through three case studies employing software engineering datasets. In case study 1, we successfully pinpointed the conditions that result in either a high or low correlation between effort and software size, offering valuable insights for software project managers. In case study 2, we effectively identified the conditions that lead to a high or low correlation between the number of bugs and source lines of code, aiding in the formulation of software test planning strategies. In case study 3, we applied our approach to the two-step software effort estimation process, uncovering the conditions most likely to yield low effort estimation errors.

Enhancing effort estimation in global software development using a unique combination of Neuro Fuzzy Logic and Deep Learning Neural Networks (NFDLNN)

ABSTRACT Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.

A personalized prognostic model for long-term survival in patients with intrahepatic cholangiocarcinoma: a retrospective cohort study.

This study aimed to determine the optimal cutoff points for age and tumor size of patients with intrahepatic cholangiocarcinoma (ICC) and to establish and verify a predictive nomogram of overall survival at 1, 3, and 5 years. From the SEER (Surveillance, Epidemiology, and End Results) database, 1,325 ICC patients were selected and randomly divided into training and testing cohorts at a 7:3 ratio. Using the X-tile software, age and tumor size were classified into 3 subgroups: ≤61, 62-74, and ≥75 years and ≤35, 36-55, and ≥56 mm. Subsequently, univariate and multivariate Cox regression analyses were performed using the R software in the training cohort to determine independent risk factors, compile the prediction nomogram, and verify it with the testing cohort findings. The C-indexes of the new prediction nomograms in the training and testing cohorts were 0.738 (95% confidence interval [CI], 0.718-0.758) and 0.750 (95% CI, 0.72-0.78), respectively. Furthermore, the areas under the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves based on the nomogram were 0.792, 0.853, and 0.838, respectively, higher than the ROC based on the 7th and 8th editions of the American Joint Cancer Commission (AJCC) staging system. This study established and verified a prognostic nomogram that improved the accuracy of the 1-, 3-, and 5-year survival predictions for ICC patients, compared with that based on the 7th and 8th editions of the AJCC staging system, and can help clinicians make personalized survival predictions.

Can instability variations warn developers when open-source projects boost?

Although architecture instability has been studied and measured using a variety of metrics, a deeper analysis of which project parts are less stable and how such instability varies over time is still needed. While having more information on architecture instability is, in general, useful for any software development project, it is especially important in Open Source Software (OSS) projects where the supervision of the development process is more difficult to achieve. In particular, we are interested when OSS projects grow from a small controlled environment (i.e., the cathedral phase) to a community-driven project (i.e., the bazaar phase). In such a transition, the project often explodes in terms of software size and number of contributing developers. Hence, the complexity of the newly added features, and the frequency of the commits and files modified may cause significant variations of the instability of the structure of the classes and packages. Consequently, in this article we analyze the instability in OSS projects, especially during that sensitive phase where they become community-driven. Our results show that instability metrics can be easily obtained in such type of transitions. We also observed from our case studies that instability metrics can help finding out the balance between adding new functionality and performing refactoring. As a conclusions we state that instability metrics offer relevant information in the transition phase from the cathedral to the bazaar.

Software Size Measurement: Bridging Research and Practice

Software Size Measurement: Bridging Research and Practice

Perancangan Sistem Informasi Hino Service on Site (Studi Kasus : Dealer Hino, PT. Persada Lampung Raya)

<span lang="EN-US">Service on site is one of the after-sales services for Hino Truck vehicles. The Service on Site contract program provides customer vehicle service services at the customer's location or site by placing a mechanic at the customer's location. In its implementation, several obstacles were encountered, such as the vehicle service history was not recorded, there were no service reports so the customer did not know the vehicle's performance. So this research develops the design of the Hino Service on Site Information System using observational research methods, literature review, and documentation. To build the system, Use Case diagrams were designed and then measured using Use Case Points (UCP) to assist management in expanding the Servicee on Site Information System. UCP will assist management when making decisions regarding system development in terms of time, human resources and finances. Software measurement using UCP in the Service on Site Information System at Hino Dealers PT. Persada Lampung Raya has a Use Case Point (UCP) score of 38.448 and is categorized as a small software size project, which is smaller than 99. With the proposed design of this system, it can simplify and speed up the on site service administration process and can provide information in the form of vehicle performance reports to customers so that they can improve service on site program</span>

A Systematic Review of AI Based Software Test Case Optimization

Software test case optimization for real-time systems is a vulnerability detection methodology that assesses the resilience of targeted programs by subjecting them to irregular input data. As the volume, size, and intricacy of software continue to escalate, conventional manual test case generation has encountered challenges like insufficient logical coverage, minimal automation levels, and inadequate test scenarios. These difficulties underscore the need for innovative approaches that maximize software dependability and performance. An artificial intelligence powered fuzzing technique, which exhibits remarkable proficiency in data analysis and classification prediction. This paper examines the recent advancements in fuzzing research and conducts a comprehensive review of artificial intelligence driven fuzzing approaches in software test cases optimization. The major review explains the test case validation workflow and discusses the optimization of distinct phases within fuzzing utilizing in the software testing. Particular emphasis is placed on the implementation of artificial intelligence in the following software testing phases. This process involves position selection, which includes organizing and cleaning data; generating test cases that cover different inputs and expected outputs; selecting fuzzy input values for testing edge cases; validating the results of each test case to ensure accuracy and reliability. Finally, it synthesizes the obstacles and complexities associated with integrating artificial intelligence into software test case optimization techniques and anticipate potential future directions in the software testing.

Exploring the Impact of Security-Based Non-Functional Requirements on Early Software Size Estimation

Exploring the Impact of Security-Based Non-Functional Requirements on Early Software Size Estimation

AneuGuide™ software-assisted vs. manual measurements in sizing for pipeline embolization device: An agreement study

Sizing of flow diverters (FDs) is a challenging task in the treatment of intracranial aneurysms due to their foreshortening behavior. The purpose of this study is to evaluate the difference between the sizing results from the AneuGuide™ software and from conventional 2D measurement. Ninety-eight consecutive patients undergoing pipeline embolization device (PED) treatment between October 2018 and April 2023 in the First Medical Center of Chinese PLA General Hospital (Beijing, China) were retrospectively analyzed. For all cases, the optimal PED dimensions were both manually determined through 2D measurements on pre-treatment 3D-DSA and computed by AneuGuide™ software. The inter-rater reliability between the two sets of sizing results for each methodology was analyzed using intraclass correlation coefficient (ICC). The degree of agreement between manual sizing and software sizing were analyzed with the Bland-Altman plot and Pearson's test. Differences between two methodologies were analyzed with Wilcoxon signed rank test. Statistical significance was defined as p < 0.05. There was better inter-rater reliability between AneuGuide™ measurements both for diameter (ICC 0.92, 95%CI 0.88–0.95) and length (ICC 0.93, 95%CI 0.89–0.96). Bland-Altman plots showed a good agreement for diameter selection between two methodologies. However, the median length proposed by software group was significantly shorter (16 mm versus 20 mm, p < 0.001). No difference was found for median diameter (4.25 mm versus 4.25 mm). We demonstrated that the AneuGuide™ software provides highly reliable results of PED sizing compared with manual measurement, with a shorter stent length. AneuGuide™ may aid neurointerventionalists in selecting optimal dimensions for FD treatment.

Information technology for prediction of software quality level

Today, there is a contradiction between the rapid increase in the complexity and size of modern software while increasing responsibility for the performance of their functions, the increasing requirements of customers and users to the quality and efficiency of software use and the imperfection of models, methods, tools of predicting software quality at the early stages of the life cycle.Therefore, the task of predicting the software quality level based on requirements is relevant. The aim of this study is to solve this task by developing information technology for prediction software quality levels based on requirements. The proposed information technology for prediction software quality level based on requirements provides analysis of quality attributes in requirements, reflects the dependence (equations) of quality characteristics on attributes, forms a quantitative assessment of quality characteristics, reflects the dependence (equation) of quality on its characteristics, forms a quantitative assessment of quality, performs quality level prediction, provides all the listed services simultaneously and the model, methods, and tools underlying the IT belong to common methodological approaches and are integrated. The developed system of predicting the software quality level based on requirements provides the user with predicted estimates of eight software quality characteristics, geometric interpretation of the software quality characteristics’ values, a comprehensive indicator of the predicted software quality, and a conclusion about the future software quality level. On the basis of this, it is possible to compare sets of requirements for software and make a reasonable choice of a set of requirements for further implementation. The information technology and the system of predicting the software quality level based on requirements, developed in this paper, provide the possibility of comparing sets of requirements for software, justified selection of requirements for further implementation of quality software (as experiments have shown, this is only one of the four proposed sets), and rejection or revision of unsuccessful sets of requirements that cannot be used to develop quality software.

A Comparative Analysis of Regression Models for Software Effort Estimation

Software Effort Estimation is the utmost task in software engineering and project management. This is important to estimate cost properly and the number of people required for a project to be developed. Many techniques have been used to estimate cost, time, schedule and required manpower for software development industries. Nowadays software is developed in a more complex way and its success depends on efficient estimation techniques. In this research, we have compared five regression algorithms on different projects to estimate software effort. The main advantage of these models is they can be used in the early stages of the software life cycle and that can be helpful to project managers to conduct effort estimation efficiently before starting the project. It avoids project overestimation and late delivery. Software size, productivity, complexity and requirement stability are the input vectors for these regression models. The estimated efforts have been calculated using Ridge Regression, Lasso Regression, Elastic Net, Random Forest and Support Vector Regression. We have compared unitedly these models for the first time as software effort estimators. R-squared Score, Mean Squared Error (MSE) and Mean Absolute Error (MAE) are calculated for these regression models. Ridge, Lasso and Elastic Net show comparatively better results among others.

Estimated Value of Software Developer Productivity at the Software Implementation Stage Using Function Points

Most Software Development Processes (SDP) project failures occur due to errors in estimating cost, time, and effort during the planning phase. This happened because the planning carried out still relied on the intuition and experience of the programmer. One approach that can be taken to plan the right SDP is to know the value of SDP productivity. The focus of this research is to determine the value of productivity based on the differences in programmers’ skills. The case study is conducted to determine the productivity value of the web-based software that has been built, namely McDelivery. The productivity value is calculated based on the ratio of software size to the effort. Software size is obtained through the calculation of the Application Function Point Count (APFC). Meanwhile, the effort is obtained through expert judgment to determine the time needed by the development team at the junior, middle, and senior software developers to implement software functionality in person-day to the form of program code. The result shows that the productivity value of SDP is directly proportional to the level of ability of the programmers. These productivity values ​​can be used as a solution option to calculate the estimated time, cost, and even the availability of programmers that are adjusted to the conditions faced in planning software development

Association of nutritional indices and prognosis of stroke patients: a systematic review and meta-analysis.

The aim of this study was to document the association between malnutrition and mortality as well as functional outcomes in patients with stroke. PubMed, Embase and Scopus databases were systematically searched for observational studies that had used either of the three nutritional indices, geriatric nutritional risk index (GNRI), prognostic nutritional index (PNI), and controlling nutritional status score (CONUT), and examined the association between malnutrition and outcomes of interest in patients with stroke. The primary outcome was mortality and secondary outcomes were risk of recurrence and functional disability. Analysis was performed using STATA 16.0 software (College Station, TX, USA) and pooled effect sizes were reported as either hazards ratio (HR) or as odds ratio (OR). Random effects model was used for the analysis. A total of 20 studies were included, of which, 15 were focused on acute ischemic stroke (AIS) patients. Among patients with AIS, moderate to severe malnutrition, assessed using CONUT (OR 4.80, 95% CI: 2.31, 9.98), GNRI (OR 3.57, 95% CI: 2.08, 6.12) and PNI (OR 8.10, 95% CI: 4.69, 14.0), was associated with increased risk of mortality within 3 months and at 1-year follow-up (CONUT: OR 2.74, 95% CI: 1.96, 3.83; GNRI: OR 2.26, 95% CI: 1.34, 3.81; PNI: OR 3.32, 95% CI: 2.24, 4.93). Patients with moderate to severe malnutrition, assessed using any of the three indices, had an increased risk of having an unfavourable outcome [modified Rankin Score (mRS) score of 3 to 6, denoting major disability and/or death] within 3 months and at 1-year follow-up. Only one study reported the risk of recurrence. Assessing malnutrition in stroke patients at the time of hospital admission using any of the three nutritional indices is useful due to the observed association of malnutrition with survival and functional outcomes. However, due to a limited number of studies, there is a need for large prospective studies to validate the findings observed in this meta-analysis.

Data-driven agile software cost estimation models for DHS and DoD

Problem:Since the Manifesto for Agile Software Development, the cost analysis community has struggled to find the most appropriate size measures for developing accurate agile software development cost estimates at an early phase to establish baseline budgets and schedules, and for the selection of competitive bidders. At this time, common agile sizing measures such as Story Points are not practical since these are reported months after contract award in the Department of Homeland Security (DHS) and Department of Defense (DoD). The problem is compounded with the lack of data to build estimation models for informed decisions. Aims:The primary objective is to investigate how well two new size measures (Functional Story and Issues) accurately relate to total effort, and how these compare and rank against four popular software size measures (Story, Story Point, Unadjusted Function Points, and Simple Function Points). The second objective is to rank the six sizing measures based on how well each estimate software development effort at an early phase or after contract award. Method:The experimental framework relied on an analysis of variance and goodness-of-fit tests to examine and compare the accuracy of effort estimation models using six competing size measures. The analysis is based on data from 17 agile projects implemented between 2014 to 2021. Results:Our two new size measures (Functional Story, Issues) proved to be good predicters of total software development effort. Functional Story is better at predicting total effort at early phase than Function Points and Story. Functional Story and Function Points are better at predicting total effort after contract award than Story Point and Issues. Conclusion:The DHS and DoD cost community can choose one or more of these estimation models to evaluate agile software development cost proposals or track agile developer’s progress after contract award.

Correctness-by-Construction

Correctness-by-Construction (CbC) is an incremental software development technique in the field of formal methods to create functionally correct programs guided by a specification. In contrast to post-hoc verification, where the specification and verification take part after implementing a program, with CbC the specification is defined first, and then the program is successively created using a small set of refinement rules that define side conditions preserving the correctness of the program. This specification-first, refinement-based approach as pursued by CbC has the advantage that errors are likely to be detected earlier in the design process and can be tracked more easily. Even though the idea of CbC emerged over 40 years ago, CbC is not widespread and is mostly used to create small algorithms.We believe in the idea of CbC and envision a scaled CbC approach that contributes to solving problems of modern software verification. In this short paper, we give an overview of our research regarding CbC in four different lines of research. For all of them, we provide tool support for building the CorC ecosystem that even further enables CbC-based development for different fields of application and size of software systems. Furthermore, we give an outlook on future work that extends on our concepts for CbC.

Effectiveness of educational interventions on knowledge and behavioral practices of adolescents for prevention of noncommunicable diseases in low- and middle-income countries: A systematic review protocol

ABSTRACT Introduction: The rising burden of noncommunicable diseases (NCDs) is a dominating public health crisis, globally causing 38 million of 57 million deaths annually, with 85% of these deaths occurring in low- and middle-income countries (LMICs). However, while addressing NCDs, adolescents have been largely overlooked, who have a notable share in the NCD burden globally. Adolescents are vulnerable for developing high-risk behaviors, which puts them at high risk of NCDs such as ischemic heart diseases, diabetes, and stroke. Objective: The objective of this systematic review will be to identify the effectiveness of educational interventions on the knowledge and behavioral practices of adolescents for the prevention of NCDs in LMICs. Methodology: Five electronic databases such as PubMed, the Cochrane Central Register of Controlled Trials, Google Scholar, Embase, and Scopus will be utilized for generating pooled evidence. Two reviewers will individually screen the titles and abstracts for eligibility, and in case of any disagreement, the help of a third reviewer will be taken. Recognized studies will be critically appraised for methodological quality using the Cochrane risk-of-bias tool. Data will be extracted from the studies included in the review for author’s name, year of study, study setting, study design, study participants and their age, sample size in the intervention group, NCD domain covered by the study intervention, intervention details, measure of outcome, etc. For data synthesis, studies will be pooled using RevMan software and effect sizes will be depicted using forest plots. The Grading of Recommendations, Assessment, Development, and Evaluation approach to grade the certainty of evidence will be followed for assessing the quality of evidence. Publication bias will also be checked using funnel plots and Egger’s test. Clinical Relevance: To the best of our knowledge, this will be the first systematic review that will assess the empirical evidence and evaluate the effectiveness of educational interventions for the prevention of NCDs among adolescents of LMICs. This review will assist the public health experts and policymakers in designing effective intervention for preventing NCDs among adolescents. Results and Conclusion: The results of this systematic review will provide an in-depth insight of the existing country-specific educational policies and interventions for the prevention of NCDs among adolescents in various LMICs. This will help in the replication of such cost-effective educational models or interventions in other LMICs followed by advocacy in their school policy reforms and other related programs focusing on NCDs.

Exploring Maintainability Index Variants for Software Maintainability Measurement in Object-Oriented Systems

During maintenance, software systems undergo continuous correction and enhancement activities due to emerging faults, changing environments, and evolving requirements, making this phase expensive and time-consuming, often exceeding the initial development costs. To understand and manage software under development and maintenance better, several maintainability measures have been proposed. The Maintainability Index is commonly used as a quantitative measure of the relative ease of software maintenance. There are several Index variants that differ in the factors affecting maintainability (e.g., code complexity, software size, documentation) and their given importance. To explore the variants and understand how they compare when evaluating software maintainability, an experiment was conducted with 45 Java-based object-oriented software systems. The results showed that the choice of the variant could influence the perception of maintainability. Although different variants presented different values when subjected to the same software, their values were strongly positively correlated and generally indicated similarly how maintainability evolved between releases and over the long term. Though, when focusing on fine-grained results posed by the Index, the variant selection had a larger impact. Based on their characteristics, behavior, and interrelationships, the variants were divided into two distinct clusters, i.e., variants that do not consider code comments in their calculation and those that do.

What really changes when developers intend to improve their source code: a commit-level study of static metric value and static analysis warning changes

Many software metrics are designed to measure aspects that are believed to be related to software quality. Static software metrics, e.g., size, complexity and coupling are used in defect prediction research as well as software quality models to evaluate software quality. Static analysis tools also include boundary values for complexity and size that generate warnings for developers. While this indicates a relationship between quality and software metrics, the extent of it is not well understood. Moreover, recent studies found that complexity metrics may be unreliable indicators for understandability of the source code. To explore this relationship, we leverage the intent of developers about what constitutes a quality improvement in their own code base. We manually classify a randomized sample of 2,533 commits from 54 Java open source projects as quality improving depending on the intent of the developer by inspecting the commit message. We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for the fine-tuning of a state-of-the art deep learning model for natural language processing. The benchmark we provide with our ground truth indicates that the deep learning model can be confidently used for commit intent classification. We use the model to increase our data set to 125,482 commits. Based on the resulting data set, we investigate the differences in size and 14 static source code metrics between changes that increase quality, as indicated by the developer, and changes unrelated to quality. In addition, we investigate which files are targets of quality improvements. We find that quality improving commits are smaller than non-quality improving commits. Perfective changes have a positive impact on static source code metrics while corrective changes do tend to add complexity. Furthermore, we find that files which are the target of perfective maintenance already have a lower median complexity than files which are the target of non-pervective changes. Our study results provide empirical evidence for which static source code metrics capture quality improvement from the developers point of view. This has implications for program understanding as well as code smell detection and recommender systems.