Phases Of Software Development Research Articles

VulGraB: Graph‐embedding‐based code vulnerability detection with bi‐directional gated graph neural network

AbstractCode vulnerabilities can have serious consequences such as system attacks and data leakage, making it crucial to perform code vulnerability detection during the software development phase. Deep learning is an emerging approach for vulnerability detection tasks. Existing deep learning‐based code vulnerability detection methods are usually based on word2vec embedding of linear sequences of source code, followed by code vulnerability detection through RNNs network. However, such methods can only capture the superficial structural or syntactic information of the source code text, which is not suitable for modeling the complex control flow and data flow and miss edge information in the graph structure constructed by the source code, with limited effect of neural network model. To solve the above problems, this article proposes a code vulnerability detection method, named VulGraB, which is based on graph embedding and bidirectional gated graph neural networks. VulGraB uses node2vec to convert the program‐dependent graphs into graph embeddings of the code, which contain rich structure information of the source code, improving the ability of features to express nonlinear information to a certain extent. Then the BiGGNN is used for training, and finally the accuracy of the detection results is evaluated using target program. The bi‐directional gated neural network utilizes a bi‐directional recurrent structure, which is beneficial to global information aggregation. The experimental results show that the accuracy of VulGraB is significantly improved over the baseline models on two datasets, with F1 scores of 85.89% and 97.24% being the highest, demonstrating that VulGraB consistently outperforms other effective vulnerability detection models.

Different Machine Learning Algorithms used for Secure Software Advance using Software Repositories

In the present phase of the Fourth Industrial Revolution (4IR or Industry 4.0), the digital world has a wealth of data, such as Internet of Things (IoT) data, cybersecurity data, mobile data, business data, social media data, health data, etc. To intelligently analyze these data and develop the corresponding smart and automated applications, the knowledge of artificial intelligence (AI), particularly, machine learning (ML) is the key. Cyber Security attacks are significantly growing in today’s modern world of technology and advanced software development. The inclusion of cyber security defense is vital in every phase of software development. Identifying and implementing key relevant cyber security vulnerability controls during the early stages of the software development life cycle, i.e., the requirement phase is especially important. The Common Attack Pattern Enumeration & Classification (CAPEC) is a publicly available software repository from MITRE that currently lists 555 vulnerability attack patterns. As Cyber Security continues to exponentially grow in complexity, the importance of the Machine Learning role to automate the identification of vulnerabilities for various software development is paramount to aid software developers in creating protected software. This paper discusses the conducted survey on different machine learning algorithms used for secure software development using software repositories.

Test case prioritization for changed code using nature inspired optimizer

The software development and maintenance phase succeeded with significant regression testing activity. The software must be re-tested every time it upgrades to preserve its quality. Software testing as a whole is an expensive and tedious task due to resource constraints. Using the prioritization technique implies regression testing to re-test software after it has been modified. In this situation, the prioritization technique can use information acquired about earlier test case executions to generate test case orderings. The approaches for test case prioritization arrange them all in such a sequence that maximizes their efficacy in accomplishing specific goals. This paper presents a hybrid technique for change-testing or regression testing through test case prioritization. The suggested method first generates the test cases, then clustered in untested and unimportant groups using kernel-based fuzzy c-means clustering technique. The appropriate test cases are then considered for prioritization using the grey wolf optimizer. The results compared with the approaches such as ant colony, particle swarm, and genetic algorithm optimization method, and it is observed that the proposed approach efficiency increased by 91% of fault detection rate.

Context-Based Aspect-Oriented Requirement Engineering Model

Mobile applications are context-oriented systems that involve the use of context information while operating. Mobile applications demand tackling context information in the early phase of software engineering. A context-aware system demands a different approach to handling the influence of the context on a system's requirements. Aspect-oriented Requirement Engineering separates concerns throughout requirements, called crosscutting concerns, in the early phase of software development to improve the modularity of complex applications. Capturing requirements embedded within context is a challenging procedure. This study aimed to identify such contextual characteristics of requirements in the early phase of software engineering, using natural language processing techniques, by proposing Context-Based Aspect-Oriented Requirement Engineering (CB-AORE) to visualize the existence of crosscutting concerns. CB-AORE performs context modeling to analyze the context dependency with base requirements and helps the analyst to visualize the correlation of functional and non-functional requirements with context. A case study analyzed the identification of context and its use to identify crosscutting concerns.

Performance assessment and analysis of development and operations based automation tools for source code management

<span lang="EN-US">Development and operations (DevOps), an accretion of automation tools, efficiently reaches the goals of software development, test, release, and delivery in terms of optimization, speed and quality. Diverse set of alternative automation tools exist for different phases of software development, for which DevOps adopts several selection criteria to choose the best tool. This research paper represents the performance evaluation and analysis of automation tools employed in the coding phase of DevOps culture. We have taken most commonly followed source code management tools-BitBucket, GitHub actions, and GitLab into consideration. Current work assesses and analyzes their performance based on DevOps evaluation criteria that too are categorized into different dimensions. For the purpose of performance evaluation, weightage and overall score is assigned to these criteria based on existing renowned literature and industrial case study of TekMentors Pvt Ltd. On the ground of performance outcome, the tool with the highest overall score is realized as the best source code automation tool. This performance analysis or measure will be a great benefit to our young researchers/students to gain an understanding of the modus operandi of DevOps culture, particularly source code automation tools. As a part of future research, other dimensions of selection criteria can also be considered for evaluation purposes.</span>

Cross-project clone consistent-defect prediction via transfer-learning method

Code clones are comparable code snippets that are introduced into software by developers in order to increase software development productivity. A change to code clone may result in a consistent-defect if the developers forget to verify the consistency of the code after the change. To reduce such change-related maintenance costs, researchers have proposed a number of methods for predicting clone consistency in advance. Unfortunately, the effectiveness of these cross-project models is unsatisfactory, and performing such predictions with insufficient data remains a challenge. Meanwhile, cross-project defect prediction via transfer learning method is prevalent in the software engineering community. Consequently, we first construct an empirical study to explore whether transfer-learning techniques could well be utilized for clone cross-project consistent-defect prediction in the initial stages of software development. In this paper, we employ transfer-learning techniques to predict clone consistency at both the time of clone creating and clone changing in order to avoid clone consistent-defects and maintenance. We conduct an experiment on open-source projects to evaluate the effectiveness of various transfer-learning methods. Our investigation demonstrates that transfer-learning techniques have a beneficial impact on predicting cross-project clone consistent-defect, and that the size of the dataset also has a positive effect on prediction. In order to promote software safety and security, we recommend that developers leverage transfer-learning to enhance the capability for clone cross-project consistent-defect prediction early in the software development phase.

Constructing a Tool for Software Regression Testing Based on Crow Search Method

The software testing phase is an essential phase of software development. Its aim is to ensure that the program meets the desired requirements of it. Through the testing process, errors are found in the programs so that they can be fixed before deployment, i.e. before being used or delivered to the customer. The program must also be tested after to be published and delivered, this is called regression testing and it is one of the basic activities in software development and it must be done in the software maintenance phase to ensure its reliability. In this research, a tool was built that selects the optimal test cases that are used in the regression testing phase, using artificial intelligence techniques. The Crow Search Algorithm was used in the test case selection, and after modifications and improvements were made to the algorithm, the Improved Crow algorithm was proposed, which generates and selects test cases that achieve the basic paths of the program based on the improved fitness function, the dynamic awareness probability value of the crow, and the spiral search mechanism for the crows. In addition, the genetic algorithm was used for these test cases prioritization.

A survey on what users think about SysML

AbstractSystems Modeling Language (SysML) has been applied in the past years to a variety of software and systems engineering projects, by hundreds of researchers, engineers, and other systems and software professionals. Thus, it is expected that all this experience has been described in research articles. Therefore, we propose a survey describing what practitioners and researchers think about this modeling language, the actual use of SysML, and how SysML is used in the software and system engineering life cycle. This article describes a survey on SysML, answering questions such as the most used diagrams for each phase of the development of a system, the most common domains, and other data about the participants. The survey was answered by 343 participants from 38 countries, mostly systems engineers, software and systems architects, and researchers. Industry and academia can use our results (i) for assisting researchers and engineers to select appropriate diagrams for each software and systems development phase, (ii) for a better understanding of which industry domains SysML is most commonly applied, (iii) as a reference for identifying which types of systems are modeled with SysML, (iv) for knowing which software tools are most used, and (v) which other modeling languages are most commonly integrated with SysML for software and systems development.

Analysis for Requirements Testing Phase of Software Development

Analysis for Requirements Testing Phase of Software Development

Heterogeneous Cross-Project Defect Prediction via Optimal Transport

Heterogeneous cross-project defect prediction (HCPDP) aims to learn a prediction model from a heterogeneous source project and then apply the model to a target project. Existing HCPDP works mapped the data of the source and target projects in a common space. However, the pre-defined forms of mapping methods often limit prediction performance and it is difficult to measure the distance between two data instances from different feature spaces. This paper introduced optimal transport (OT) theory for the first time to build the relationship between source and target data distributions, and two prediction algorithms were proposed based on OT theory. In particular, an algorithm based on the entropic Gromov-Wasserstein (EGW) discrepancy was developed to perform the HCPDP model. The proposed EGW model measures the distance between two metric spaces by learning an optimal transfer matrix with the minimum data transfer cost and avoids measuring the distance of two instances of different feature spaces. Then, to improve EGW performance, an EGW+ transport algorithm based on EGW was developed by integrating target labels. Experimental results showed the effectiveness of EGW and EGW+ methods, and proved that our methods can support developers to find the defects in the early phase of software development.

HEDF: A Method for Early Forecasting Software Defects Based on Human Error Mechanisms

As the primary cause of software defects, human error is the key to understanding, and perhaps to predicting and avoiding them. Little research has been done to predict defects on the basis of the cognitive errors that cause them. This paper proposes an approach to predicting software defects through knowledge about the cognitive mechanisms of human errors. Our theory is that the main process behind a software defect is that an error-prone scenario triggers human error modes, which psychologists have observed to recur across diverse activities. Software defects can then be predicted by identifying such scenarios, guided by this knowledge of typical error modes. The proposed idea emphasizes predicting the exact location and form of a possible defect. We conducted two case studies to demonstrate and validate this approach, with 55 programmers in a programming competition and 5 analysts serving as the users of the approach. We found it impressive that the approach was able to predict, at the requirement phase, the exact locations and forms of 7 out of the 22 (31.8%) specific types of defects that were found in the code. The defects predicted tended to be common defects: their occurrences constituted 75.7% of the total number of defects in the 55 developed programs; each of them was introduced by at least two persons. The fraction of the defects introduced by a programmer that were predicted was on average (over all programmers) 75%. Furthermore, these predicted defects were highly persistent through the debugging process. If the prediction had been used to successfully prevent these defects, this could have saved 46.2% of the debugging iterations. This excellent capability of forecasting the exact locations and forms of possible defects at the early phases of software development recommends the approach for substantial benefits to defect prevention and early detection.

Design and Programming for Multicore machines: An Empirical study on time and effort required by programmer

As the demand for high-performance computing continues to surge, harnessing the full potential of multicore architectures has become paramount. This paper explores a pragmatic approach to transition from sequential to parallel programming, capitalizing on the computational prowess of modern hardware systems. Recognizing the challenges of enforcing parallelism in early software development phases, we advocate for a focus on the implementation stage, where architects, designers, and developers can seamlessly introduce parallel constructs while preserving software integrity. To facilitate this paradigm shift, we introduce the “SDLC model with Parallel Constructs,” a modified Software Development Life Cycle (SDLC) framework comprising additional phases: “Parallel Constructs” and “Test Parallel Constructs.” This model empowers development teams to integrate parallel computing efficiently, enhancing performance and maintaining a structured development process. Our observations reveal intriguing dynamics. Initially, the single-threaded program outperforms its parallel counterpart for smaller datasets, but as data sizes grow, the parallel version demonstrates superior performance. We underscore the pivotal role of available CPU cores and task partitioning in determining efficiency. Our analysis also evaluates the programmer’s effort, measured by lines of code, needed for the transition. Leveraging OpenMP constructs streamlines this transition, reducing programming complexity.

SOFTWARE QUALITY PREDICTION USING MACHINE LEARNING TECHNIQUES AND SOURCE CODE METRICS: A REVIEW

Software quality prediction is the Machine Learning (ML) based technique in which ML models are trained using historical data. Output from these quality models can be used by software experts in the early phase of software development for improving the quality of software by controlling the various quality attributes like maintainability, reliability, security issues of software etc. In this study a systematic review of studies from 2005 to 2021 is performed. Studies that use ML techniques and source code metrics for Software Quality Prediction (SQP) are included for review. Study assesses the commonly used machine learning techniques and source code metric for SQP. Commonly used datasets, feature selection techniques and commonly used performance measures in software quality prediction are also assessed. In this paper 53 primary studies are selected for systematic review. Results of this study prove that Bayesian Learning (BL), Regression, Ensemble Learning (EL), Decision Tree (DT) and Support Vector Machine (SVM) are most commonly ML techniques used for quality prediction which comprises 58%, 52%, 41%, 32%, and 32% of the overall studies respectively. It is also assessed that NASA, PROMISE, Apache, Mozilla Firefox and Eclipse are the most commonly used datasets for training and testing the SQP models. LOC, CC, CBO, RFC, WMC, LCOM, DIT and NOC are among the most commonly used source code metrics in SQP. Based on the results from the selected studies it is concluded that ML techniques and source code metrics have the ability to improve the overall quality of the software.

Development of an mHealth App-Based Intervention for Depressive Rumination (RuminAid): Mixed Methods Focus Group Evaluation.

Depression is a common mental health condition that poses a significant public health burden. Effective treatments for depression exist; however, access to evidence-based care remains limited. Mobile health (mHealth) apps offer an avenue for improving access. However, few mHealth apps are informed by evidence-based treatments and even fewer are empirically evaluated before dissemination. To address this gap, we developed RuminAid, an mHealth app that uses evidence-based treatment components to reduce depression by targeting a single key depressogenic process-rumination. The primary objective of this study was to collect qualitative and quantitative feedback that could be used to improve the design of RuminAid before the software development phase. We reviewed empirically supported interventions for depression and rumination and used the key aspects of each to create a storyboard version of RuminAid. We distributed an audio-guided presentation of the RuminAid storyboard to 22 individuals for viewing and solicited user feedback on app content, design, and perceived functionality across 7 focus group sessions. The consumer-rated quality of the storyboard version of RuminAid was in the acceptable to good range. Indeed, most participants reported that they thought RuminAid would be an engaging, functional, and informational app. Likewise, they endorsed overwhelming positive beliefs about the perceived impact of RuminAid; specifically, 96% (21/22) believed that RuminAid will help depressed ruminators with depression and rumination. Nevertheless, the results highlighted the need for improved app aesthetics (eg, a more appealing color scheme and modern design). Focus group members reported that the quality of information was quite good and had the potential to help adults who struggle with depression and rumination but expressed concern that poor aesthetics would interfere with users' desire to continue using the app. To address these comments, we hired a graphic designer and redesigned each screen to improve visual appeal. We also removed time gating from the app based on participant feedback and findings from related research. These changes helped elevate RuminAid and informed its initial software build for a pilot trial that focused on evaluating its feasibility and acceptability.

Software assistants in software engineering: A systematic mapping study

AbstractThe increasing essential complexity of software systems makes current software engineering methods and practices fall short in many occasions. Software assistants have the ability to help humans achieve a variety of tasks, including the development of software. Such assistants, which show human‐like competences such as autonomy and intelligence, help software engineers do their job by empowering them with new knowledge. This article investigates the research efforts that have been conducted on the creation of assistants for software design, construction and maintenance paying special attention to the user‐assistant interactions. To this end, we followed the standard systematic mapping study method to identify and classify relevant works in the state of the art. Out of the 7580 articles resulting from the automatic search, we identified 112 primary studies that present works which qualify as software assistants. We provide all the resources needed to reproduce our study. We report on the trends and goals of the assistants, the tasks they perform, how they interact with users, the technologies and mechanisms they exploit to embed intelligence and provide knowledge, and their level of automation. We propose a classification of software assistants based on interactions and present an analysis of the different automation patterns. As outcomes of our study, we provide a classification of software assistants dealing with the design, construction and maintenance phases of software development, we discuss the results, identify open lines of work and challenges and call for new innovative and rigorous research efforts in this field.

A Systematic Mapping Study on Quality Attribute Traceability

Quality attribute traceability (QAT), as a sub-discipline of software traceability, ensures that quality attributes are considered in all phases of software development. QAT is one of the most critical and challenging research areas in requirement engineering. In recent years, several studies have been conducted on quality attribute traceability. However, researchers in this field have problems to identifying the research status of this field due to the lack of a comprehensive secondary study that reveals research trends and gaps. Evidence-based software engineering (EBSE) proposes systematic mapping study (SMS) and systematic literature review (SLR) methods for secondary studies on software engineering. In this paper we use the semantically enhanced SMS method in the QAT research area. In order to improve SMS, we introduce a concept classification approach. In this approach by using a QAT domain ontology, the keywords classification process is improved. The most important innovation of this study is the presentation of the QAT research tree. This research tree has been created by gathering research topics identified in this field along with existing gaps. Identified research topics include “QAT Approach,” “QAT Development,” “QAT Model,” “QAT Framework,” and “QAT Algorithm”. The results also indicate that “QAT Frameworks” is a research trend, and most recent studies have focused on this topic. On the other hand, “Trace Maintenance” and “Trace Integrity” are the research gaps in this field. In this article, active researchers, journals, conferences and countries in the field of QAT are also introduced. We also introduce a set of tools that facilitate the SMS process.

Exploring Design smells for smell-based defect prediction

Defect prediction is commonly used to reduce the effort from the testing phase of software development. A promising strategy is to use machine learning techniques to predict which software components may be defective. Features are key factors to the prediction’s success, and thus extracting significant features can improve the model’s accuracy. In particular, code smells are a category of those features that have been shown to improve the prediction performance significantly. However, Design code smells, a state-of-the-art collection of code smells based on the violations of the object-oriented programming principles, have not been studied in the context of defect prediction. In this paper, we study the performance of defect prediction models by training multiple classifiers for 97 real projects. We compare using Design code smells as features and using other Traditional smells from the literature and both. Moreover, we cluster and analyze the models’ performance based on the categories of Design code smells. We conclude that the models trained with both the Design code smells and the smells from the literature performed the best, with an improvement of 4.1% for the AUC score, compared to models trained with only Traditional smells. Consequently, Design smells are a good addition to the smells commonly studied in the literature for defect prediction.

Web-Based Cooperation Information System at The Science Techno Park Technology Business Development Center

Science Techno Park (STP) Unand's partnership with various government, business, and legal institutions will continue for the foreseeable future in order to disseminate innovators' knowledge to the larger community. The issue that frequently arises is that the file search procedure is tough to locate. Furthermore, the enormous number of inventors at Andalas University are recorded in Microsoft Excel files, making it difficult for users to locate inventory data. The data that is then entered is frequently duplicated, leaving the user perplexed about the information presented. As a result, at STP Unand's Technology Business Development Center, a web-based cooperative information system was created. The goal is for STP Unand's engagement with partners to be automated, and for data collecting on the number of inventors in Unand to be structured. The SDLC (Software Development Life Cycle) method is used in this study. SDLC is a software development cycle that encompasses software development techniques, phases, and tools. The waterfall paradigm was used in the design of this application. the waterfall method was successfully used to build or design a collaborative information system application. Based on the outcomes of each stage of the waterfall method, namely analysis, design, coding, and system testing, it is possible to conclude that the design of this collaborative information system application has been successfully built in accordance with needs.

Construction and Validation of Early Software Size Estimation Models Based on ADAF-Adjusted ACD Metrics

Abstract Software size estimation is a vital activity of software project planning and management. Early software size estimation is a challenging task due to the limited information available during the early phases of software development. The goal of this paper is to construct and validate early software size estimation models based on four analysis-to-design adjustment factor (ADAF)-adjusted analysis class diagram metrics (i.e. ADAF-adjusted number of classes, ADAF-adjusted number of attributes, ADAF-adjusted number of methods and ADAF-adjusted number of relationships) using stepwise multiple linear regression and leave-one-out cross-validation. Furthermore, the prediction accuracy of the best-performing proposed model is also compared with the model based on objective class points. The results of this comparison reveal that our proposed method reduces errors significantly (i.e. on average, 16% reduction in mean absolute residual and 24% reduction in mean squared error).

Deep Learning-Based Bug Report Summarization Using Sentence Significance Factors

During the maintenance phase of software development, bug reports provide important information for software developers. Developers share information, discuss bugs, and fix associated bugs through bug reports; however, bug reports often include complex and long discussions, and developers have difficulty obtaining the desired information. To address this issue, researchers proposed methods for summarizing bug reports; however, to select relevant sentences, existing methods rely solely on word frequencies or other factors that are dependent on the characteristics of a bug report, failing to produce high-quality summaries or resulting in limited applicability. In this paper, we propose a deep-learning-based bug report summarization method using sentence significance factors. When conducting experiments over a public dataset using believability, sentence-to-sentence cohesion, and topic association as sentence significance factors, the results show that our method outperforms the state-of-the-art method BugSum with respect to precision, recall, and F-score and that the application scope of the proposed method is wider than that of BugSum.