Software Development Challenges Research Articles

Intelligent Learning-Based Methods for Determining the Ideal Team Size in Agile Practices.

One of the significant challenges in scaling agile software development is organizing software development teams to ensure effective communication among members while equipping them with the capabilities to deliver business value independently. A formal approach to address this challenge involves modeling it as an optimization problem: given a professional staff, how can they be organized to optimize the number of communication channels, considering both intra-team and inter-team channels? In this article, we propose applying a set of bio-inspired algorithms to solve this problem. We introduce an enhancement that incorporates ensemble learning into the resolution process to achieve nearly optimal results. Ensemble learning integrates multiple machine-learning strategies with diverse characteristics to boost optimizer performance. Furthermore, the studied metaheuristics offer an excellent opportunity to explore their linear convergence, contingent on the exploration and exploitation phases. The results produce more precise definitions for team sizes, aligning with industry standards. Our approach demonstrates superior performance compared to the traditional versions of these algorithms.

Improving with Hybrid Feature Selection in Software Defect Prediction

Software defect prediction (SDP) is used to identify defects in software modules that can be a challenge in software development. This research focuses on the problems that occur in Particle Swarm Optimization (PSO), such as the problem of noisy attributes, high-dimensional data, and premature convergence. So this research focuses on improving PSO performance by using feature selection methods with hybrid techniques to overcome these problems. The feature selection techniques used are Filter and Wrapper. The methods used are Chi-Square (CS), Correlation-Based Feature Selection (CFS), and Forward Selection (FS) because feature selection methods have been proven to overcome data dimensionality problems and eliminate noisy attributes. Feature selection is often used by some researchers to overcome these problems, because these methods have an important function in the process of reducing data dimensions and eliminating uncorrelated attributes that can cause noisy. Naive Bayes algorithm is used to support the process of determining the most optimal class. Performance evaluation will use AUC with an alpha value of 0.050. This hybrid feature selection technique brings significant improvement to PSO performance with a much lower AUC value of 0.00342. Comparison of the significance of AUC with other combinations shows the value of FS PSO of 0.02535, CFS FS PSO of 0.00180, and CS FS PSO of 0.01186. The method in this study contributes to improving PSO in the SDP domain by significantly increasing the AUC value. Therefore, this study highlights the potential of feature selection with hybrid techniques to improve PSO performance in SDP.

Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

Quantum software systems represent a new realm in software engineering, utilizing quantum bits (Qubits) and quantum gates (Qgates) to solve the complex problems more efficiently than classical counterparts. Agile software development approaches are considered to address many inherent challenges in quantum software development, but their effective integration remains unexplored. This study investigates key causes of challenges that could hinders the adoption of traditional agile approaches in quantum software projects and develop an Agile-Quantum Software Project Success Prediction Model (AQSSPM). Firstly, we identified 19 causes of challenging factors discussed in our previous study, which are potentially impacting agile-quantum project success. Secondly, a survey was conducted to collect expert opinions on these causes and applied Genetic Algorithm (GA) with Naive Bayes Classifier (NBC) and Logistic Regression (LR) to develop the AQSSPM. Utilizing GA with NBC, project success probability improved from 53.17 to 99.68%, with cost reductions from 0.463 to 0.403%. Similarly, GA with LR increased success rates from 55.52 to 98.99%, and costs decreased from 0.496 to 0.409% after 100 iterations. Both methods result showed a strong positive correlation (rs = 0.955) in causes ranking, with no significant difference between them (t = 1.195, p = 0.240 > 0.05). The AQSSPM highlights critical focus areas for efficiently and successfully implementing agile-quantum projects considering the cost factor of a particular project.

Software Estimation in the Design Stage with Statistical Models and Machine Learning: An Empirical Study

Accurate estimation of software effort and time in the software development process is a key activity to achieve the necessary product quality. However, underestimation or overestimation of effort has become a key challenge for software development. One of the main problems is the estimation with metrics from late stages, because the product must already be finished to make estimates. In this paper, the use of statistical models and machine learning approaches for software estimation are used in early stages such as software design, and a data set is presented with metric values of design artifacts with 37 software projects. As results, models for the estimation of development time and effort are proposed and validated through leave-one-out cross-validation. Further, machine learning techniques were employed in order to compare software projects estimations. Through the statistical tests, it was proven that the errors were not statistically different with the regression models for effort estimation. However, with Random Forest the best statistical results were obtained for estimating development time.

ПРОГРАМНЕ ЗАБЕЗПЕЧЕННЯ РОЗУМНОГО ГОДИННИКА ДЛЯ САМОСТІЙНИХ ЗАНЯТЬ СПОРТОМ ТА ФІТНЕСОМ

According to available statistics, the most popular smart watch among users is the Apple Watch. Today, there are more than 100 million unique users of this device, 75% of whom use it for sports. With a wide array of different sensors to track a user's physical parameters, neither Apple nor third-party developers have yet developed software to systematize all the collected data to improve an athlete's physical pa-rameters and achieve personal athletic goals. The purpose of the research is to find the possibility of improving the physical parameters of a novice athlete by means of a comprehensive analysis of his activity data collected by a smart watch and creating more personalized recommendations during training based on this data. There are many fitness apps available for the Apple Watch, each with its own unique features and features. Unfortunately, none of the analyzed applications pro-vide sufficient information regarding the correct execution of training and the collec-tion of indicators, which is a disadvantage for users who want to do sports without risks to their health. One of the main challenges of software development for smartwatches is the lack of extensive monetization opportunities. This led to the lack of interest of large companies and teams in this type of software. Small screen size and limited process-ing power compared to desktop or mobile devices are also issues. This means that developers must optimize both the user interface and the algorithms of their software product to increase the speed of the application. Another challenge is the diversity of the smartwatch market, where different devices run on different operating systems and have different hardware specifications. You have to consider the specifics and limitations of each device and platform and make sure that your apps are optimized for each of them, or focus on building your software products for only a limited num-ber of devices. Due to the lack of tools for developing applications for several platforms at once, the path of developing an application for only one platform - for WatchOS - was chosen. Also, one of the goals is to create an application that is completely autono-mous from a smartphone. For this, a simple and minimalistic interface and a simple algorithm for analyzing training data have been developed. The proposed application is focused on running. Statistics will be collected dur-ing the first few training sessions. After starting the activity, the application will start displaying all the standard information, such as pace, heart rate, activity time, dis-tance traveled. This data comes from the sensors and modules of the smart watch. When basic training statistics are collected, the app will begin to display tips on how to continue training.

A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions

Artificial Intelligence (AI), as one of the most important fields of computer science, plays a significant role in the software development life cycle process, especially in the implementation phase, where developers require considerable effort to convert software requirements and design into code. Automated Code Generation (ACG) using AI can help in this phase. Automating the code generation process is becoming increasingly popular as a solution to address various software development challenges and increase productivity. In this work, we provide a comprehensive review and discussion of traditional and AI techniques used for ACG, their challenges, and limitations. By analysing a selection of related studies, we will identify all AI methods and algorithms used for ACG, extracting the evaluation metrics and criteria such as Accuracy, Efficiency, Scalability, Correctness, Generalization, and more. These criteria will be used to perform a comparative result for AI methods used for ACG, exploring their applications, strengths, weaknesses, performance, and future applications.

Integrating Generative AI for Advancing Agile Software Development and Mitigating Project Management Challenges

Integrating Generative AI for Advancing Agile Software Development and Mitigating Project Management Challenges

SmartDelta project: Automated quality assurance and optimization across product versions and variants

Software systems are often built in increments with additional features or enhancements on top of existing products. This incremental development may result in the deterioration of certain quality aspects. In other words, the software can be considered an evolving entity emanating different quality characteristics as it gets updated over time with new features or deployed in different operational environments. Approaching software development with this mindset and awareness regarding quality evolution over time can be a key factor for the long-term success of a company in today’s highly competitive market of industrial software-intensive products. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a software system. To address this challenge, the multinational SmartDelta project develops automated solutions for the quality assessment of product deltas in a continuous engineering environment. The project provides smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for the next builds. This paper presents the challenges in incremental software development tackled in the scope of the SmartDelta project, and the solutions that are produced and planned in the project, along with the industrial impact of the project for software-intensive industrial systems.

Role of GenAI in Automated Code Generation within DevOps Practices: Explore how Generative AI

Artificial Intelligence (AI) is a pivotal domain within computer science, profoundly influencing the software development lifecycle, particularly during the implementation phase. Here, developers grapple with the task of translating software requirements and designs into executable code. Automated Code Generation (ACG) leveraging AI emerges as a promising solution in this context. The automation of code generation processes is gaining traction as a means to tackle diverse software development challenges while boosting productivity. This paper presents a thorough review and discourse on both traditional and AI-driven techniques employed in ACG, highlighting their respective challenges and constraints. Through an examination of pertinent literature, we identify various AI methodologies and algorithms utilized in ACG, extracting evaluation metrics such as Accuracy, Efficiency, Scalability, Correctness, Generalization, among others. These metrics serve as the basis for a comparative analysis of AI-driven ACG methods, delving into their applications, strengths, weaknesses, performance metrics, and future prospects.

Model transformation and code generation using a secure business process model

Emerging daily, new devices and software-driven advancements pose challenges in software development, including errors, bugs, and evolving requirements. This leads to delays in delivery. Ensuring software security within the Software Development Life Cycle (SDLC) is crucial. To address this, the research focuses on incorporating security aspects early in the SDLC through model transformation. Platform-independent models with security attributes like Integrity, Privacy, Security Audit, non-repudiation, and authentication are generated. A template-based source code generator is utilized to create the structure of the source model. The Secure Business Process Model (SBPM) encompasses Unified Modeling Language (UML) artifacts, such as analysis level classes and sequence diagrams, enriched with security attributes derived from the activity model. Security requirements are linked to elements extracted from the source model, and structural codes with security-enabled members are produced. Automation in software development is inevitable, though not complete, as it plays a vital role in addressing these challenges and improving the security of software applications.

CharM — Evaluating a model for characterizing service-based architectures

Service-based architecture is an approach that emerged to overcome software development challenges such as difficulty to scale, low productivity, and strong dependence between elements. Microservice, an architectural style that follows this approach, offers advantages such as scalability, agility, resilience, and reuse. This architectural style has been well accepted and used in industry and has been the target of several academic studies. However, analyzing the state-of-the-art and -practice, we can notice a fuzzy limit when trying to classify and characterize the architecture of service-based systems. Furthermore, it is possible to realize that it is difficult to analyze the trade-offs to make decisions regarding the design and evolution of this kind of system. Some concrete examples of these decisions are related to how big the services should be, how they communicate, and how the data should be divided/shared. Based on this context, we developed the CharM, a model for characterizing the architecture of service-based systems that adopts microservices guidelines. To achieve this goal, we followed the guidelines of the Design Science Research in five iterations, composed of an ad-hoc literature review, discussions with experts, two case studies, and a survey. As a contribution, the CharM is an easily understandable model that helps professionals with different profiles to understand, document, and maintain the architecture of service-based systems.

A systematic decision-making framework for tackling quantum software engineering challenges

Quantum computing systems harness the power of quantum mechanics to execute computationally demanding tasks more effectively than their classical counterparts. This has led to the emergence of Quantum Software Engineering (QSE), which focuses on unlocking the full potential of quantum computing systems. As QSE gains prominence, it seeks to address the evolving challenges of quantum software development by offering comprehensive concepts, principles, and guidelines. This paper aims to identify, prioritize, and develop a systematic decision-making framework of the challenging factors associated with QSE process execution. We conducted a literature survey to identify the challenging factors associated with QSE process and mapped them into 7 core categories. Additionally, we used a questionnaire survey to collect insights from practitioners regarding these challenges. To examine the relationships between core categories of challenging factors, we applied Interpretive Structure Modeling (ISM). Lastly, we applied fuzzy TOPSIS to rank the identified challenging factors concerning to their criticality for QSE process. We have identified 22 challenging factors of QSE process and mapped them to 7 core categories. The ISM results indicate that the ‘resources’ category has the most decisive influence on the other six core categories of the identified challenging factors. Moreover, the fuzzy TOPSIS indicates that ‘complex programming’, ‘limited software libraries’, ‘maintenance complexity’, ‘lack of training and workshops’, and ‘data encoding issues’ are the highest priority challenging factor for QSE process execution. Organizations using QSE could consider the identified challenging factors and their prioritization to improve their QSE process.

Challenges of Working from Home in Software Development During Covid-19 Lockdowns

The COVID-19 pandemic in 2020/2021/2022 and the resulting lockdowns forced many companies to switch to working from home, swiftly, on a large scale, and without preparation. This situation created unique challenges for software development, where individual software professionals had to shift instantly from working together at a physical venue to working remotely from home. Our research questions focus on the challenges of software professionals who work from home due to the COVID-19 pandemic, which we studied empirically at a German bank. We conducted a case study employing a mixed methods approach. We aimed to cover both the breadth of challenges via a quantitative survey, as well as a deeper understanding of these challenges via the follow-up qualitative analysis of 15 semi-structured interviews. In this article, we present the key impediments employees faced during the crisis, as well as their similarities and differences to the known challenges in distributed software development (DSD). We also analyze the employees’ job satisfaction and how the identified challenges impact job satisfaction. In our study, we focus on challenges in communication, collaboration, tooling, and management. The findings of the study provide insights into this emerging topic of high industry relevance. At the same time, the study contributes to the existing academic research on work from home and on the COVID-19 pandemic aftermath.

The FastLanes Compression Layout: Decoding > 100 Billion Integers per Second with Scalar Code

The open-source FastLanes project aims to improve big data formats, such as Parquet, ORC and columnar database formats, in multiple ways. In this paper, we significantly accelerate decoding of all common Light-Weight Compression (LWC) schemes: DICT, FOR, DELTA and RLE through better data-parallelism. We do so by re-designing the compression layout using two main ideas: (i) generalizing the value interleaving technique in the basic operation of bit-(un)packing by targeting a virtual 1024-bits SIMD register, (ii) reordering the tuples in all columns of a table in the same Unified Transposed Layout that puts tuple chunks in a common "04261537" order (explained in the paper); allowing for maximum independent work for all possible basic SIMD lane widths: 8, 16, 32, and 64 bits. We address the software development, maintenance and future-proofness challenges of increasing hardware diversity, by defining a virtual 1024-bits instruction set that consists of simple operators supported by all SIMD dialects; and also, importantly, by scalar code. The interleaved and tuple-reordered layout actually makes scalar decoding faster, extracting more data-parallelism from today's wide-issue CPUs. Importantly, the scalar version can be fully auto-vectorized by modern compilers, eliminating technical debt in software caused by platform-specific SIMD intrinsics. Micro-benchmarks on Intel, AMD, Apple and AWS CPUs show that FastLanes accelerates decoding by factors (decoding >40 values per CPU cycle). FastLanes can make queries faster, as compressing the data reduces bandwidth needs, while decoding is almost free.

Decision Making of Agile Patterns in Offshore Software Development Outsourcing: A Fuzzy Logic-Based Analysis

Computation intelligence techniques are important for making decisions in an agile-based offshore software development paradigm. Offshore development faces additional challenges, such as trust, communication and coordination, and socio-cultural and knowledge transfer. There is a need to determine the rankings of challenges considering their criticality concerning practitioners working in agile-based offshore software development. This paper aims to identify and rank agile challenges in offshore software development by applying computational intelligence techniques. From the systematic literature review, we identified 30 communication and coordination challenges. The distributed agile pattern catalog consists of 15 patterns, from which eight were used to solve communication and collaboration challenges. Many researchers have used fuzzy logic to quantify their results. We further applied the fuzzy analytical technique to determine the priority order concerning the criticality of the identified agile pattern catalog. The results showed that Central Code Repository Pattern ranked the most significant for solving communication and coordination challenges. Global Scrum Board Pattern and Synchronous Communication Pattern ranked second.

CEDR: A Compiler-integrated, Extensible DSSoC Runtime

In this work, we present a C ompiler-integrated, E xtensible D omain Specific System on Chip R untime (CEDR) ecosystem to facilitate research toward addressing the challenges of architecture, system software, and application development with distinct plug-and-play integration points in a unified compile time and runtime workflow. We demonstrate the utility of CEDR on the Xilinx Zynq MPSoC-ZCU102 for evaluating performance of pre-silicon hardware in the trade space of SoC configuration, scheduling policy and workload complexity based on dynamically arriving workload scenarios composed of real-life signal processing applications scaling to thousands of application instances with Fast Fourier Transform and matrix multiply accelerators. We provide insights into the tradeoffs present in this design space through a number of distinct case studies. CEDR is portable and has been deployed and validated on Odroid-XU3, X86, and Nvidia Jetson Xavier-based SoC platforms. Taken together, CEDR is a capable environment for enabling research in exploring the boundaries of productive application development, resource management heuristic development, and hardware configuration analysis for heterogeneous architectures.

MoTrans-BDI: Leveraging the Beliefs-Desires-Intentions agent architecture for collaborative model transformation by example

Due to the growing interest in model-driven system development techniques, the efficient design of automated model transformations between heterogeneous models has become a major challenge in software development. While a number of specialized languages have been proposed, aiming at specifying model transformations, there is currently no matured foundation for specifying transformations between such models that are based on designers/experts collaboration in order to propose a solution that satisfies users’ requirements and application constraints. This transformation process is a complex task and must emulate how designers and experts with different perspectives behave and reflect about model transformations. In this paper, we propose a framework based on a novel approach for the specification and design of model transformations called MoTrans-BDI, which leverages Evolutionary Multi-Agent System (EMAS) to simulate designers’ expertise for the transformation of models. Our approach is based on the Belief-Desire-Intention (BDI) agent model and the Contract Net Protocol where agents’ beliefs feed from a series of transformation examples. The emphasis of using the specific model is the opportunity to produce a target model that may be composed of parts from different experts’ designs. We first experimentally evaluate MoTrans-BDI on twelve handmade UML2REL model transformation problems. All types of agents are able to produce perfect target models compared to human experts’ collaboratively-produced target models. Second, we empirically evaluate MoTrans-BDI’s proposal in terms of response time on Star Schema Benchmark (SSB) queries. An application of MoTrans-BDI in the case of UML2REL transformation is performed to stress and highlight the transformation process steps.

Software Cost Estimation Practices Using RASCH Measurement Model: An Indonesian Regional Government Evidence

Software cost estimation (SCE) can be substantial challenges in software development as it could yield inaccurate results. The SCE failure influences project sustainability which might lead to additional costs and times to complete the project. Previous research has provided empirical evidence that the unsuccessfulness of software cost estimation in public sectors is higher than in private sectors. This is due to the actual cost not in line with the estimation cost. The objective of this paper is to determine the SCE practices in the regional government of Indonesia. This research adopts both quantitative and qualitative approaches. The quantitative approach was conducted by distributing questionnaires to government employees who are involved in cost estimation. RASCH model software was used to analyze the data. The qualitative approach involved interviewing personnel in the cost estimation process in regional government. This research involved seven government agencies in West Sumatera Province, Indonesia. The findings of this study show that current practices of SCE in the regional government of Indonesia are not effective although there is a budget ceiling (pagu anggaran) and owner estimate cost (harga perkiraan sendiri). This paper highlights the reasons for inaccurate results of SCE in public sector due to five factors which are: people who have authority to estimate the software cost, time of the estimation, no tool to estimate the software cost, the frequency of changing the scope and requirements of the project, and the number of changes of scope and requirements. However, this research may not include all the factors that influence software cost estimation in government projects yet. There might be other factors that might influence SCE in the public sector. These factors can assist the regional government of Indonesia to effectively and quantitatively analyze the factors that significantly impact software development in Indonesian regional government context. Those factors can be included as parameters in estimating the software cost and also to enhance the software cost estimation process. Thus, it can reduce the risk of the project from overrunning and over budget.

Prediction of software defects by knowledge graph and genetic algorithm

Software defect detection is one of the biggest software development challenges and accounts for the largest budget in the software development process. One of the effective activities for software development and increasing its reliability is to predict software defects before reaching the test stage, which helps to save time in the production, maintenance and cost process. This research aims to present a software defect prediction method based on knowledge graphs and automated machine learning. We use knowledge acquisition, knowledge fusion, knowledge storage and knowledge calculation and other knowledge map construction technology research, to realize the knowledge map recommends high-quality software defect prediction models as the hot-start input conditions for automatic search. The empirical study uses NASA's open-source dataset experimental objects and six performance evaluation indicators include Precision, Recall, PRC (Precision Recall Characteristic), ROC (Receiver Operating Characteristic), F-Measure, MCC (Matthews Correlation Coefficient). The experimental results show that the proposed model performs better than the traditional classic software defect prediction model recommended by the knowledge map in terms of different datasets and evaluation indicators

Does Routine Order Variation and Bot Usage Spur Resilience in Open Source Communities?

Resilience is important for Open Source Software Development (OSSD) teams to overcome software development challenges that commonly occur in the platforms as a result of unexpected events such as natural disasters, cyber-attacks, pandemics etc., As OSSD teams primarily rely on voluntary contributions, any type of disruptions to the routines of open source workers can lead to loss of productivity and cause delays in open software production and release cycles. Currently we do not have sufficient understanding about how OSSD teams bounce back from a disruption and become resilient. To understand the effects of disruption we chose a popular open source community, called Kubernetes, to understand the resilience process in OSSD teams. We collected data from an exemplar event of COVID-19 pandemic to understand how Kubernetes community became resilient after the unprecedented announcement of the global pandemic by World Health Organization (WHO). Overall, we collected over .4 million records corresponding to the activities of 65 Kubernetes teams from March 2020 to May 2020. We use organizational routine lens to understand how routine order variation (the way in which the activities’ order varies) and bot usage can have effects on resilience in open source communities. The results highlight the importance of order variation and bot usage for OSSD teams’ resilience and provide implications for open source project management practises during crisis situations.