Software Development Tasks Research Articles

Refining software defect prediction through attentive neural models for code understanding

Identifying defects through manual software testing is a resource-intensive task in software development. To alleviate this, software defect prediction identifies code segments likely to contain faults using data-driven methods. Traditional techniques rely on static code metrics, which often fail to reflect the deeper syntactic and semantic features of the code. This paper introduces a novel framework that utilizes transformer-based networks with attention mechanisms to predict software defects. The framework encodes input vectors to develop meaningful representations of software modules. A bidirectional transformer encoder is employed to model programming languages, followed by fine-tuning with labeled data to detect defects. The performance of the framework is assessed through experiments across various software projects and compared against baseline techniques. Additionally, statistical hypothesis testing and an ablation study are performed to assess the impact of different parameter choices. The empirical findings indicate that the proposed approach can increase classification accuracy by an average of 15.93% and improve the F1 score by up to 44.26% compared to traditional methods.

Preemptive Software Project Scheduling Considering Personality Traits

The success of software projects is inseparable from the effective scheduling of multi-skilled human resources. In software development, employees’ personality traits often affect the performance of their work. Additionally, software developers are usually multi-tasking, which makes software development tasks preemptive. In view of this, we investigate the preemptive software project scheduling problem considering personality traits (PSPSP-PT), which aims at assigning employees with different skills and personality traits to suitable tasks and determining the start time of each task with the consideration of preemption, multi-skill, and precedence relations such that the total cost of the project is minimized. We formulate a mixed-integer linear programming model for the PSPSP-PT and design a dual priority rules-based heuristic (DPRH). Based on a benchmark dataset that is generated using a full factorial design, computational experiments are conducted to analyze the performance of our DPRH. Comparative analysis with existing algorithms reveals that our DPRH is competitive in terms of efficiency and effectiveness.

Comparing emotions in ChatGPT answers and human answers to the coding questions on Stack Overflow.

Recent advances in generative Artificial Intelligence (AI) and Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs) and AI-powered chatbots like ChatGPT, which have numerous practical applications. Notably, these models assist programmers with coding queries, debugging, solution suggestions, and providing guidance on software development tasks. Despite known issues with the accuracy of ChatGPT's responses, its comprehensive and articulate language continues to attract frequent use. This indicates potential for ChatGPT to support educators and serve as a virtual tutor for students. To explore this potential, we conducted a comprehensive analysis comparing the emotional content in responses from ChatGPT and human answers to 2000 questions sourced from Stack Overflow (SO). The emotional aspects of the answers were examined to understand how the emotional tone of AI responses compares to that of human responses. Our analysis revealed that ChatGPT's answers are generally more positive compared to human responses. In contrast, human answers often exhibit emotions such as anger and disgust. Significant differences were observed in emotional expressions between ChatGPT and human responses, particularly in the emotions of anger, disgust, and joy. Human responses displayed a broader emotional spectrum compared to ChatGPT, suggesting greater emotional variability among humans. The findings highlight a distinct emotional divergence between ChatGPT and human responses, with ChatGPT exhibiting a more uniformly positive tone and humans displaying a wider range of emotions. This variance underscores the need for further research into the role of emotional content in AI and human interactions, particularly in educational contexts where emotional nuances can impact learning and communication.

A3Test: Assertion-Augmented Automated Test case generation

Context:Test case generation is a critical yet challenging task in software development. Recently, AthenaTest – a Deep Learning (DL) approach for generating unit test cases has been proposed. However, our revisiting study reveals that AthenaTest can generate less than one-fifth of the test cases correctly, due to a lack of assertion knowledge and test signature verification. Objective:This paper introduces A3Test, a novel DL-based approach to the generation of test cases, enhanced with assertion knowledge and a mechanism to verify consistency of the name and signatures of the tests. A3Test aims to adapt domain knowledge from assertion generation to test case generation. Method:A3Test employs domain adaptation principles and introduces a verification approach to name consistency and test signatures. We evaluate its effectiveness using 5,278 focal methods from the Defects4j dataset. Results:Our findings indicate that A3Test outperforms AthenaTest and ChatUniTest. A3Test generates 2.16% to 395.43% more correct test cases, achieves 2.17% to 34.29% higher method coverage, and 25.64% higher line coverage. A3Test achieves 2.13% to 12.20% higher branch coverage, 2.22% to 12.20% higher mutation scores, and 2.44% to 55.56% more correct assertions compared to both ChatUniTest and AthenaTest respectively for one iteration. When generating multiple test cases per method A3Test still shows improvements and comparable efficacy to ChatUnitTest. A survey of developers reveals that the majority of the participants 70.51% agree that test cases generated by A3Test are more readable than those generated by EvoSuite. Conclusions:A3Test significantly enhances test case generation through its incorporation of assertion knowledge and test signature verification, contributing to the generation of correct test cases.

An Empirical Study on the Correctness and Effort to Integrate Feature Models

Feature model integration is pivotal in software development, particularly in evolving software product lines through new feature accommodations. Despite its significance, the influence of developers’ experience on integration efforts and correctness still needs to be more adequately understood. This study conducted a controlled experiment with 25 participants (18 students and seven professionals) following well-known guidelines to run empirical studies. Each participant addressed ten experimental tasks, encompassing 250 integration scenarios, to explore two research questions. The effort and correctness rate in integrating feature models were quantified, revealing that students exerted higher effort (29.23%) and achieved a higher number of correct integrations (39.53%) than professionals. Notably, this superiority lacked statistical significance. Additionally, this article highlights practical implications and noteworthy challenges for the scientific community, providing valuable insights for software development practices. The findings lay a foundation for future studies, delving into software development tasks where students and professionals may achieve comparable results. Finally, this study marks an initial step towards an ambitious agenda, empirically advancing the feature model integration field.

Project-specific code summarization with in-context learning

Automatically generating summaries for source code has emerged as a valuable task in software development. While state-of-the-art (SOTA) approaches have demonstrated significant efficacy in summarizing general code, they seldom concern code summarization for a specific project. Project-specific code summarization (PCS) poses special challenges due to the scarce availability of training data and the unique styles of different projects. In this paper, we empirically analyze the performance of Large Language Models (LLMs) on PCS tasks. Our study reveals that using appropriate prompts is an effective way to solicit LLMs for generating project-specific code summaries. Based on these findings, we propose a novel project-specific code summarization approach called P-CodeSum. P-CodeSum gathers a repository-level pool of (code, summary) examples to characterize the project-specific features. Then, it trains a neural prompt selector on a high-quality dataset crafted by LLMs using the example pool. The prompt selector offers relevant and high-quality prompts for LLMs to generate project-specific summaries. We evaluate against a variety of baseline approaches on six PCS datasets. Experimental results show that the P-CodeSum improves the performance by 5.9% (RLPG) to 101.51% (CodeBERT) on BLEU-4 compared to the state-of-the-art approaches in project-specific code summarization.

Gamifying software engineering subject to enhance the quality of knowledge

AbstractGamification has been widely used in education in recent years. Gamification has been proven to be a useful tool for students, as it motivates them and helps them to learn. The lack of motivation on the part of the students and their poor perception of the importance of software engineering has led to the definition of SESGA. This game has been designed and carried out on third year students of the computer science degree. SESGA has been designed for students to improve their knowledge of static code analysis, unit testing and Git. A software engineering project was carried out in groups, in which each of the students had to perform common software development tasks. The evaluation has measured the accomplishment, challenge, competence, guidance, immersion, playfulness and social involvement of SESGA obtaining remarkable results. It has also shown that SESGA has allowed students to have better academic results and the number of failures has decreased.

WELL: Applying bug detectors to bug localization via weakly supervised learning

AbstractBug localization, which is used to help programmers identify the location of bugs in source code, is an essential task in software development. Researchers have already made efforts to harness the powerful deep learning (DL) techniques to automate it. However, training bug localization model is usually challenging because it requires a large quantity of data labeled with the bug's exact location, which is difficult and time‐consuming to collect. By contrast, obtaining bug detection data with binary labels of whether there is a bug in the source code is much simpler. This paper proposes a WEakly supervised bug LocaLization (WELL) method, which only uses the bug detection data with binary labels to train a bug localization model. With CodeBERT finetuned on the buggy‐or‐not binary labeled data, WELL can address bug localization in a weakly supervised manner. The evaluations on three method‐level synthetic datasets and one file‐level real‐world dataset show that WELL is significantly better than the existing state‐of‐the‐art model in typical bug localization tasks such as variable misuse and other bugs.

DEVELOPMENT OF SYSTEM SOFTWARE BASED ON THE CHATGPT CHAT-BOT

The article discusses the main aspects of system software development, namely writing program code for a simple device driver using the ChatGPT chat bot. The introduction outlines the main difficulties (hardware, software, complexity of debugging and testing, and security issues) that developers may encounter during driver development, as well as ways to possibly solve them: for example, by increasing the level of their theoretical knowledge or using modern tools, such as neural networks. The main part of the article reviews existing research in the field of system software development and testing. Existing software development tools are reviewed: such as development environments, tools for debugging, profiling, testing and static analysis of program code, as well as solutions in the field of version control and collaboration. A comparative table is provided showing the pros and cons of developing device drivers for Windows and Linux operating systems. The stages of the process of generating driver program code for a computer mouse using the ChatGPT chat bot are described. Conventionally, the article identifies five main stages: preparing training data, training, creating a query, generating and repeating stages in case of an unsatisfactory result. Examples of generated program code for a computer mouse driver for Windows and Linux operating systems are provided. In conclusion, conclusions are given regarding further prospects for using the ChatGPT chatbot in system software development tasks.

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.

Применение метода распознавания именованных сущностей в сфере информационных технологий для анализа задач по разработке программного обеспечения

Explores the application of the Named Entity Recognition method to analyze software development tasks. It describes the main methods of labor-intensive analysis of IT companies, as well as NER technologies, libraries and frameworks such as SpaCy, DeepPavlov, Natasha and Pullenti, which can be used for extracting information about named entities from text data in Russian. This article offers useful resources and approaches for analyzing development tasks, which helps to improve planning, project management and reduce development time.

Traceability of Architectural Design Decisions and Software Artifacts: A Systematic Mapping Study

Abstract The definition of architecture is a crucial task in software development, where the architect is responsible for making the right decisions to meet specific functional and quality requirements. These architectural design decisions form the foundation that shapes the arrangement of elements within a system. Unfortunately, these decisions are often poorly documented, implicit in various artifacts, or inadequately updated, leading to negative consequences on the maintainability of a system and resulting in rework and cost overruns. The objective of this systematic mapping study is to comprehend the current state regarding approaches for traceability of architectural design decisions and how these decisions are linked with the different artifacts used in software development. To achieve this, an information extraction protocol is followed, utilizing databases with search strings, inclusion, and exclusion criteria. The findings demonstrate that this knowledge is highly relevant; however, it is rarely explicitly documented. As a result, most works propose diverse approaches to extract this knowledge from existing technical documentation, commonly used tools, and other sources of product and process information. In contrast, it is evident that there is no standard for documenting design decisions, leading each author to present a subjective version of what is important and where to trace these decisions. This suggests that there is still a significant amount of research to be conducted regarding the traceability of these architectural design decisions and their connection with software artifacts. Such research could lead to intriguing new proposals for investigation.

Learning from West African storytellers

Several research works propose non-textual alternatives to textual documentation and similar forms of representing information in software development. This is because of the problems that stem from writing these documents, which range from incomprehensible requirements to ambiguous user stories. The various proposals of researchers often contain some trace of oral or visual communication. In this paper, we study the implications of eliminating textual communication and substituting unnecessary writing by extracting the values of West African oral storytellers. Traditional West African communities did not make use of writing for thousands of years and yet their legends, customs, beliefs, and knowledge were effectively transmitted across several generations. How did they manage to accomplish this? What can we learn from their storytellers? How can these lessons be applied to software products? These are all questions that this paper attempts to answer. Perhaps if we fully understand how they operated, then we can target our written communication to the activities where it is needed instead of spreading writing across plenty of tasks as it is currently. To achieve this, we performed an analysis of the two domains: West African oral storytelling and software development and found similarities, then selected some key elements from oral storytelling and explained how they can have relevance in software development. The theme directly encompasses diversity and inclusion by bringing into software engineering a perspective of a region where its literacy research is scarcely being explored. The study found that traditional oral storytelling can provide insights into effective communication and audience engagement, and identified four ways in which software development can be compared to oral storytelling. The study also found that certain elements of storytelling, such as audience relationship, story structure, parables and proverbs, and community relaxation and support, can be applied to writing tasks in software development.

On transforming model‐based tests into code: A systematic literature review

SummaryModel‐based test design is increasingly being applied in practice and studied in research. Model‐based testing (MBT) exploits abstract models of the software behaviour to generate abstract tests, which are then transformed into concrete tests ready to run on the code. Given that abstract tests are designed to cover models but are run on code (after transformation), the effectiveness of MBT is dependent on whether model coverage also ensures coverage of key functional code. In this article, we investigate how MBT approaches generate tests from model specifications and how the coverage of tests designed strictly based on the model translates to code coverage. We used snowballing to conduct a systematic literature review. We started with three primary studies, which we refer to as the initial seeds. At the end of our search iterations, we analysed 30 studies that helped answer our research questions. More specifically, this article characterizes how test sets generated at the model level are mapped and applied to the source code level, discusses how tests are generated from the model specifications, analyses how the test coverage of models relates to the test coverage of the code when the same test set is executed and identifies the technologies and software development tasks that are on focus in the selected studies. Finally, we identify common characteristics and limitations that impact the research and practice of MBT: (i) some studies did not fully describe how tools transform abstract tests into concrete tests, (ii) some studies overlooked the computational cost of model‐based approaches and (iii) some studies found evidence that bears out a robust correlation between decision coverage at the model level and branch coverage at the code level. We also noted that most primary studies omitted essential details about the experiments.

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.

Identifying and Addressing Problems in the Estimation Process: a Case Study Applying Action Research

Literature shows that a large part of software projects exceeds the amount of effort and estimation duration, even though we currently witness an evolution of software project management discipline. Through its best practices, software engineering tries to reduce the flaws in software development. Several techniques and resources have been presented to help to reduce this problem. This paper aims to propose an approach based on action research to improve the estimation process in software development tasks by identifying problems. A case study is carried out to show the effectiveness of our approach. The results show an improvement of 50% accuracy over the baseline estimation process.

FORMATION OF COMPETENCIES OF COLLECTIVE SOFTWARE DEVELOPMENT OF STUDENTS IN THE FIELD OF INFORMATION TECHNOLOGY IN THE PROCESS OF USING FREE SOFTWARE

The article analyzes the existing systems that provide an opportunity for collective software development by students of information technology and offers recommendations for their use in the educational process. In the authors' opinion, it is advisable to use free versions of the Atlassian stack (Jira, Bitbucket, Confluence), which allows forming professional competencies in collaborative software development of future specialists, while the limitations of free versions are not critical for educational purposes.
 The concept of the competence of collective software development of an information technology specialist is formulated, by which we mean the integrated ability of an individual to effectively solve software development tasks in the process of teamwork in professional activities.
 It has been found that software development requires the implementation of many steps, from defining requirements to testing and releasing a product. In the process of forming a collective software development of an information technology specialist, cloud services and other systems (Bitbucket, GitHub, GitLab, Jira, Asana, Basecamp, Mantis, Bugzilla, etc.) are used.
 Training future IT professionals in the development process and involvement in real software development projects will help them acquire, develop, and improve their collaborative software development competencies.

A parallel deep learning-based code clone detection model

Code clone detection is a crucial task in software development and maintenance. While deep learning-based methods have been proposed to tackle this problem, most of them neglect the time and memory consumption issues which can be significant when working with limited computational resources. Given the inability of recurrent neural networks to train in a parallel manner, this paper presents a parallel code clone detection model based on temporal convolutional networks. The proposed method splits the corresponding abstract syntax tree into a set of code statement sequences, utilizes a temporal convolutional neural network to generate representations containing complexity features found in the source code, and finally measures the distance between these representations. The proposed method is evaluated on a real-world dataset for code clone detection, and the experimental results demonstrate that it performs comparably to state-of-the-art methods while requiring significantly less time and memory costs.

Affordances and agency in students’ use of online platforms and resources beyond curricular boundaries

ABSTRACT This study explored how agency is expressed when undergraduate students cross curricular boundaries to learn software development by realising affordances enabled by resources on online platforms. The study employed a qualitative research design based on individual stimulated recall interviews with 27 computer and software engineering students. The hybrid thematic analysis employed an ecological framework in which the actions the students performed during software development tasks were interpreted in relational terms. The results reveal multiple interrelated affordances that were realised as the students learnt by using various resources on different online platforms. Agency was expressed primarily through students pursuing specific objectives related to their learning across platforms and curricular boundaries and was shared and distributed across people and the environment. The findings highlight the idea that boundaries are formed and crossed based on how people experience the environment, which, in turn, is enabled or constrained by the resources used.

Understanding the Role of Stack Overflow in Supporting Software Development Tasks: A Research Perspective

Stack Overflow is a Q&A website that is popular among developers and extensively used in software engineering (SE) research. A significant body of research has examined how Stack Overflow can assist with software development tasks, such as recommending APIs. However, while researchers have recognized the importance of Stack Overflow in SE research related to software development tasks, the specific ways in which it is utilized and the reasons for its widespread usage in research have not been thoroughly explored. To address these knowledge gaps, we conducted the first study to understand the role of Stack Overflow in assisting with SE research regarding software development tasks by systematically examining relevant and high-quality research works. Meanwhile, we carried out a qualitative survey to gain insight into why researchers choose to utilize Stack Overflow in SE research and to solicit suggestions for the better use of Stack Overflow in research. The study identifies trends in the research area, prominent researchers and organizations, and the types of tasks that utilize Stack Overflow in research, with coding and debugging being the most common. Moreover, it examines how Stack Overflow data is utilized in SE research regarding software development tasks, including searching, training models, and mining associations. Our qualitative survey of researchers indicates that the popularity of Stack Overflow stems from its comprehensive explanations of technical topics that are often not found in documentation or manuals. The findings provide a comprehensive understanding of the role of Stack Overflow in SE research regarding software development tasks, and offer actionable implications for both researchers and stakeholders of Stack Overflow to facilitate future research and improvements.