Software Development Activities Research Articles

Effort-Aware Fault-Proneness Prediction Using Non-API-Based Package-Modularization Metrics

Source code complexity of legacy object-oriented (OO) software has a trickle-down effect over the key activities of software development and maintenance. Package-based OO design is widely believed to be an effective modularization. Recently, theories and methodologies have been proposed to assess the complementary aspects of legacy OO systems through package-modularization metrics. These package-modularization metrics basically address non-API-based object-oriented principles, like encapsulation, commonality-of-goal, changeability, maintainability, and analyzability. Despite their ability to characterize package organization, their application towards cost-effective fault-proneness prediction is yet to be determined. In this paper, we present theoretical illustration and empirical perspective of non-API-based package-modularization metrics towards effort-aware fault-proneness prediction. First, we employ correlation analysis to evaluate the relationship between faults and package-level metrics. Second, we use multivariate logistic regression with effort-aware performance indicators (ranking and classification) to investigate the practical application of proposed metrics. Our experimental analysis over open-source Java software systems provides statistical evidence for fault-proneness prediction and relatively better explanatory power than traditional metrics. Consequently, these results guide developers for reliable and modular package-based software design.

Requirements for modelling tools for teaching

Modelling is an important activity in software development and it is essential that students learn the relevant skills. Modelling relies on dedicated tools and these can be complex to install, configure, and use—distracting students from learning key modelling concepts and creating accidental complexity for teachers. To address these challenges, we believe that modelling tools specifically aimed at use in teaching are required. Based on discussions at a working session organised at MODELS 2023 and the results from an internationally shared questionnaire, we report on requirements for such modelling tools for teaching. We also present examples of existing modelling tools for teaching and how they address some of the requirements identified.

Metaheuristic-based cost-effective predictive modeling for DevOps project success

Over the decade, DevOps practices have gained popularity within software development organizations for managing the dynamic behavior of system development. Implementing DevOps introduces risks and challenges that increase the difficulties in software development activities, which consequently leads to project failures. This study presents a probability-based predictive model to estimate the success or failure of DevOps projects based on the 13 most significant features identified from literature and data gathered from the DevOps practitioners using a survey questionnaire. The Naïve Bayes Classifier and Logistic Regression (LR) models allied with Grey Wolf Optimization (GWO) have been used to measure the efficiency with the cost of implementing DevOps practices. The results of the study highlighted that NBC with GWO increased the success probability from 0.4954 to 0.9971, with the cost rising from 0.2577 to 0.5000. Similarly, LR with GWO also presented an increase in success probability from 0.2880 to 0.9839, along with an increase in cost from 0.2423 to 0.3558. In conclusion, the developed prediction model based on identified features could help DevOps software development practitioners to implement DevOps projects cost-effectively and successfully.

Long-term software fault prediction with wavelet shrinkage estimation

Wavelet shrinkage estimation received considerable attentions to estimate stochastic processes such as a non-homogeneous Poisson process in a non-parametric way, and was applied to software reliability estimation/prediction. However, it lacks the prediction ability for unknown future patterns in long term and penalizes assessing the software reliability in practice. In this paper, we focus on the long-term prediction of the number of software faults detected in the testing phase and propose many novel long-term prediction methods based on the wavelet shrinkage estimation. The fundamental idea is to adopt both the denoised fault-count data and prediction values, and to minimize several kinds of loss functions to make effective predictions. We also develop an automated wavelet-based software reliability assessment tool, W-SRAT2, which is a drastic extension of the existing tool, W-SRAT, by adding those prediction algorithms. In numerical experiments with 6 actual software development project data, we investigate the predictive performance of our long-term prediction approaches, which consist of 2,640 combinations, and compare them with the common software reliability growth models with the maximum likelihood estimation. It is shown that our wavelet shrinkage estimation/prediction methods outperform the existing software reliability growth models.

Feature importance for software development effort estimation using multi level ensemble approaches

Feature importance strategy that substantially impacts software development effort estimation (SDEE) can help lower the dimensionality of dataset size. SDEE models developed to estimate effort, time, and wealth required to accomplish a software product on a limited budget are used more frequently by project managers as decision-support tool effort estimation algorithms trained on a dataset containing essential elements to improve their estimation accuracy. Earlier research worked on creating and testing various estimation methods to get accurate. On the other hand, ensemble produces superior prediction accuracy than single approaches. Therefore, this study aims to identify, develop, and deploy an ensemble approach feasible and practical for forecasting software development activities with limited time and minimum effort. This paper proposed a collaborative system containing a multi-level ensemble approach. The first level grabs the optimal features by adopting boosting techniques that impact the decided target; this subset features forward to the second level developed by a stacked ensemble to compute the product development effort concerning lines of code (LOC) and actual. The proposed model yields high accuracy and is more accurate than distinct models.

EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging study.

Cognitive human error and recent cognitive taxonomy on human error causes of software defects support the intuitive idea that, for instance, mental overload, attention slips, and working memory overload are important human causes for software bugs. In this paper, we approach the EEG as a reliable surrogate to MRI-based reference of the programmer's cognitive state to be used in situations where heavy imaging techniques are infeasible. The idea is to use EEG biomarkers to validate other less intrusive physiological measures, that can be easily recorded by wearable devices and useful in the assessment of the developer's cognitive state during software development tasks. Herein, our EEG study, with the support of fMRI, presents an extensive and systematic analysis by inspecting metrics and extracting relevant information about the most robust features, best EEG channels and the best hemodynamic time delay in the context of software development tasks. From the EEG-fMRI similarity analysis performed, we found significant correlations between a subset of EEG features and the Insula region of the brain, which has been reported as a region highly related to high cognitive tasks, such as software development tasks. We concluded that despite a clear inter-subject variability of the best EEG features and hemodynamic time delay used, the most robust and predominant EEG features, across all the subjects, are related to the Hjorth parameter Activity and Total Power features, from the EEG channels F4, FC4 and C4, and considering in most of the cases a hemodynamic time delay of 4 seconds used on the hemodynamic response function. These findings should be taken into account in future EEG-fMRI studies in the context of software debugging.

Analyzing the Tower of Babel with Kaiaulu

Context:An extensive body of work has examined socio-technical activities in software development; however, the availability of tools to enable these studies is limited. Aim:We extend Kaiaulu, a software package for Mining Software Repositories to enable a broad spectrum analysis of Social Smells and Motifs. Methods:We perform a literature review to identify what tools are available which implement graph construction methods and social smell metrics, contextualizing the contributions of our tool. Results:The few tools identified in the literature either leverage fewer parts of the software ecosystem, have been archived, or depend on components no longer maintained. Conclusion:The socio-technical features in Kaiaulu complement existing tools and related literature, while providing a simple architecture to facilitate ease or use, and ease of learning, benefitting reproducibility. Tool Repository:github.com/sailuh/kaiaulu

Genetic Programming in Software Engineering: A Systematic Literature Review

Software Engineering is present in all the Software Development Life Cycle phases, implying a systematic and disciplined development process. Nowadays, there are optimization problems within the phases and activities of Software Engineering, problems in which a Genetic Programming (GP) approach can give better results due to its representation of individuals. This Systematic Literature Review aims to analyze the current state of the application of GP in Software Engineering by collecting the phases and activities of software development where Genetic Programming has been used and summarizing the advantages of using this technique. The results show that GP has been used in every phase of software development, although mainly in the construction phase. In addition, a promising area is Program Synthesis, which substantially impacts new fields such as Genetic Improvement.

Summary of the 5th International Workshop on Automated andverifiable Software sYstem DEvelopment (ASYDE)co-located with the 38th IEEE/ACM ASE 2023

Over the past three decades, automation in software development has gone mainstream. Software development teams strive to automate as much of the software development activities as possible, spanning requirements specification, system modeling, code generation, testing, deployment, verification, as well as release phases, project status reporting and system maintenance. Automation helps to reduce development time and cost, as well as to concentrate knowledge by bringing quality into every step of the development process. The Workshop on Automated and verifiable Software sYstem DEvelopment (ASYDE) provided a forum to share and discuss innovative contributions to research and practice related to novel software engineering approaches to automated and verifiable development of software systems. The 5th edition took place on September 11th, 2023, in Kirchberg, Luxembourg. Notably, this marked the inaugural co-location of ASYDE with the IEEE/ACM International Conference on Automated Software Engineering (ASE).

A new taxonomy of global software development best practices using prioritization based on a fuzzy system

AbstractEffective management of development projects is crucial to delivering high‐quality software within time and budget constraints. However, organizing geographically distributed software development activities presents unique challenges, including difficulties in face‐to‐face interaction and coordination. To assist the global software development community in updating and developing new project management techniques, identifying and prioritizing best practices is essential. This study aims to develop a taxonomy based on the prioritization of management practices for software development, drawing from empirical data. Fifty‐five best practices associated with the Project Management Body of Knowledge (PMBOK) fields were identified from the existing literature. The study also empirically examines the acceptability and relevance of these practices within the industry. An analytic hierarchy process within a fuzzy system is employed to prioritize these practices based on their importance in managing global software development.

An effective framework to improve the managerial activities in global software development

Abstract Global Software Development (GSD) is a contemporary approach to software development that offers numerous advantages, including enhanced cost-effectiveness and timely delivery. It enables access to a vast pool of skilled developers and facilitates the exchange of best practices and innovative ideas within the software industry. However, effective project management plays a vital role in ensuring successful product development. Organizations that achieve project success consistently adhere to well-defined project management methodologies, resulting in desired outcomes within predefined time frames and allocated resources. The success rate of software projects significantly increases with diligent software management efforts. Nevertheless, the distributed nature of GSD presents significant challenges related to collaboration, information dissemination, and process control, which ultimately impede effective development and compromise software quality. In this study, we identify various challenges associated with the GSD process and propose strategies to overcome obstacles to effective project management. Additionally, we introduce a comprehensive framework designed to enhance managerial activities in GSD.

Capturing User Experience of Customer-Centric Software Process through Requirement Process: Systematic Review

Agile and User Experience have become popular for decades due to the ability to understand customer needs. However, both methods have different perspectives on the point of view, value, and quality. Moreover, user research in UX is usually conducted in the long term. The human aspect is a critical thing in Agile, the purpose of this aspect is to understand the value and need of the product, and with the user stories, several developers try to understand the human aspect of customers. In the elicitation process of the UX, developers used user stories to capture customer personality. One important factor is emotion; UX researchers measure emotions from the product journey, but it is unpleasant when the customer finds out the product does not meet expectations. This study aims to research the implementation of capturing emotion in user experience among Agile software development activities from several perspectives. In addition, Limited resources in software projects require innovation that can guarantee the sustainability and quality of the product. In this paper, we used modified systematic mapping to extract, classify, and interpret articles from popular publishers and map the user experience life cycle to answer several existing problems. This research shows that a combination of user requirement and UX increase the product's usability. Moreover, involving the user in the development center increases the project's success.

Big Code Search: A Bibliography

Code search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. Code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better code search performance. However, it is still challenging to illustrate a comprehensive view of the field considering that existing studies generally explore narrow and limited subsets of used components. This study aims to devise a grounded approach to understanding the procedure for code search and build an operational taxonomy capturing the critical facets of code search techniques. Additionally, we investigate evaluation methods, benchmarks, and datasets used in the field of code search.

Requirements quality research: a harmonized theory, evaluation, and roadmap

High-quality requirements minimize the risk of propagating defects to later stages of the software development life cycle. Achieving a sufficient level of quality is a major goal of requirements engineering. This requires a clear definition and understanding of requirements quality. Though recent publications make an effort at disentangling the complex concept of quality, the requirements quality research community lacks identity and clear structure which guides advances and puts new findings into an holistic perspective. In this research commentary, we contribute (1) a harmonized requirements quality theory organizing its core concepts, (2) an evaluation of the current state of requirements quality research, and (3) a research roadmap to guide advancements in the field. We show that requirements quality research focuses on normative rules and mostly fails to connect requirements quality to its impact on subsequent software development activities, impeding the relevance of the research. Adherence to the proposed requirements quality theory and following the outlined roadmap will be a step toward amending this gap.

Integrating Interactive Detection of Code Smells into Scrum: Feasibility, Benefits, and Challenges

(Context) Code smells indicate poor coding practices or design flaws, suggesting deeper software quality issues. While addressing code smells promptly improves software quality, traditional detection techniques often fail in continuous detection during software development. (Problem Statement) More recently, Interactive Detection (ID) technique has been proposed, enabling the detection of code smells continuously. Although the use of this technique by developers and organizations is promising, there are no practical recommendations for its use in the context of software development. (Goal) The objective of this study was to propose and evaluate the integration of ID into the widely adopted Scrum framework for agile software development. (Method) To achieve this objective, we utilized a mixed-method approach that combined a comprehensive literature review and expert knowledge to propose the integration. Furthermore, we conducted a focus group and a controlled experiment involving software development activities to evaluate this integration. (Results) The findings revealed that this integration significantly benefitted software development, such as early detection of code smells, increased effectiveness in code smell detection, and improved code quality. These findings shed light on the potential benefits of adopting this integration, offering valuable insights for developers and researchers. (Conclusions) This research emphasized the importance of continuous code smell detection as an integral part of agile development and opened avenues for further research in code quality management within agile methodologies.

Factors Affecting the Adoption of Cloud for Software Development: A Case from Turkey

Cloud-based solutions for software development activities have been emerging in the last decade. This study aims to develop a hybrid technology adoption model for cloud use in software development activities. It is based on Technology Acceptance Model (TAM), Technology–Organization–Environment (TOE) framework, and the proposed extension Personal–Organization–Project (POP) structure. The methodology selected is a questionnaire-based survey and data are collected through personally administered questionnaire sessions with developers and managers, resulting in 268 responses regarding 84 software development projects from 30 organizations in Turkey, selected by considering company and project sizes and geographical proximity to allow face-to-face response collection. Structural Equation Modeling (SEM) is used for statistical evaluation and hypothesis testing. The final model was reached upon modifications and it was found to explain the intention to adopt and use the cloud for software development meaningfully. To the best of our knowledge, this is the first study to identify and understand factors that affect the intention of developing software on the cloud. The developed hybrid model was validated to be used in further technology adoption studies. Upon modifying the conceptual model and discovering new relations, a novel model is proposed to draw the relationships between the identified factors and the actual use, intention to use and perceived suitability. Practical and social implications are drawn from the results to help organizations and individuals make decisions on cloud adoption for software development.

A Study of Software Defect Prediction Using Artificial Intelligent and Business Risk Management Approach

Abstract: The usage of several artificial intelligence algorithms in the area of software defect prediction is reviewed in the literature in this paper, it review the use of various techniques for artificial intelligence in the field of software defect prediction. Software system flaws remain a significant issue, and modern software development now faces many difficulties. Identification, estimation, and evaluation of the risks are crucial steps in order to avoid, minimize, and monitor the risks and their effects.This literature review is quite helpful because it improves understanding of the studied area. A software fault is a flaw in an executable product that results in software failure. Saving money, time, and effort may be accomplished by identifying general software process areas that may require attention from the start of a project and understanding the potential causes of problems. Planning, managing, and carrying out software development activities could be aided by the ability for early fault estimation. In order to study how many prediction approaches have been developed over this time to address the problems linked to software defect, this research conducts a literature review of works from the previous many years.In the field of software development, the study also discusses software risk management. This study makes a significant contribution by proposing a method for analyzing, organizing, and utilizing information technology initiatives in the context of software defect prediction and risk management.

Student Project-Based Space Vector Modulation Technique for Power Electronics Laboratory

Two-level DC/AC inverter topologies are widely used for low voltage and high voltage applications in power systems and industrial areas. Space Vector Modulation (SVM) is a popular Pulse-Width Modulation technique used for controlling the inverters and providing the efficient energy conversion from DC sources. However, applications of SVM-based studies are limited in the Power Electronics Laboratory (PEL) due to the vital risks associated with high voltage applications, and it is not easily learned through mathematical analysis and visual learning without implementation by undergraduate students. A simulation and experimental setup of an SVM-controlled two-level, three-phase inverter was presented in this study for undergraduate students to learn its basics in the PEL. Several programs were used to simulate the inverter in the classroom environment and to design a power circuit and microcontroller-based printed circuit board of the inverter for PEL experiments. The two case studies were given. In the case results, the output voltage waveforms of simulation and experimental inverters were compared to show the validation of simulation results. With this study, the students’ experience is enhanced in electronic circuit design, programming, coordination with hardware and software development activities, self-learning, and teamwork. Additionally, practical applications increase undergraduate students’ interest in Power Electronics Courses and reinforce their knowledge from lecture and laboratory studies.

An investigation of causes and effects of trust in Boundary Artefacts

Boundary Artefacts (BAs) support software development activities in many aspects because it carries lots of information in the same object that can be used and interpreted by several social groups within an organisation. When the BAs are inconsistent regarding their content, such as many meanings or lack of contextual information, their efficiency is reduced because stakeholders won’t trust them. This study aimed to understand the implications of differences in the perception of trust on software projects and their influence on stakeholders’ behaviour. We conducted an exploratory case study to observe the creation and utilisation of one specific BA and the implications of differences in trust and their influence on stakeholders’ behaviour. : Our investigation has shown that practitioners adding and adjusting existing content do not entirely understand the stakeholders’ needs. Together with the partial management of the content, trust is impacted. When the content of BAs does not meet the trust factors, specifically reliability and predictability, the stakeholders can’t execute their tasks appropriately, and several implications affect the software development project. Additionally, they create workarounds to supply their needs. The differences in trust in BAs affect software projects in different areas of the organisation and interfere with the task execution of various stakeholders. The decrease in trust results from inconsistencies in the content associated with the lack of management of the BA. A structured strategy for representing and managing a BA’s content seems appropriate to increase trust levels and efficiency.

Design and Development of Artificial Intelligence Knowledge Processing System for Optimizing Security of Software System

Software security vulnerabilities are significant for the software development industry. Exploration is conducted for software development industry landscape, software development eco-system landscape, and software system customer landscape. The focus is to explore the data sources that can provide the software development team with insights to act upon the security vulnerabilities proactively. Across these modules of software landscape, customer landscape, and industry landscape, data sources are leveraged using artificial intelligence approaches to identify the security insights. The focus is also on building a smart knowledge management system that integrates the information processed across modules into a central system. This central intelligence system can be further leveraged to manage software development activities proactively. In this exploration, machine learning and deep learning approaches are devised to model the data and learn from across the modules. Architecture for all the modules and their integration is also proposed. Work helps to envision a smart system for Artificial Intelligence-based knowledge management for managing software security vulnerabilities.