Software Engineering Activities Research Articles

An Empirical Study on GitHub Pull Requests’ Reactions

The pull request mechanism is commonly used to propose source code modifications and get feedback from the community before merging them into a software repository. On GitHub, practitioners can provide feedback on a pull request by either commenting on the pull request or simply reacting to it using a set of pre-defined GitHub reactions, i.e., “Thumbs-up”, “Laugh”, “Hooray”, “Heart”, “Rocket”, “Thumbs-down”, “Confused”, and “Eyes”. While a large number of prior studies investigated how to improve different software engineering activities (e.g., code review and integration) by investigating the feedback on pull requests, they focused only on pull requests’ comments as a source of feedback. However, the GitHub reactions, according to our preliminary study, contain feedback that is not manifested within the comments of pull requests. In fact, our preliminary analysis of six popular projects shows that a median of 100% of the practitioners who reacted to a pull request did not leave any comment suggesting that reactions can be a unique source of feedback to further improve the code review and integration process. To help future studies better leverage reactions as a feedback mechanism, we conduct an empirical study to understand the usage of GitHub reactions and understand their promises and limitations. We investigate in this article how reactions are used, when and who use them on what types of pull requests, and for what purposes. Our study considers a quantitative analysis on a set of 380 k reactions on 63 k pull requests of six popular open-source projects on GitHub and three qualitative analyses on a total number of 989 reactions from the same six projects. We find that the most common used GitHub reactions are the positive ones (i.e., “Thumbs-up”, “Hooray”, “Heart”, “Rocket”, and “Laugh”). We observe that reactors use positive reactions to express positive attitude (e.g., approval, appreciation, and excitement) on the proposed changes in pull requests. A median of just 1.95% of the used reactions are negative ones, which are used by reactors who disagree with the proposed changes for six reasons, such as feature modifications that might have more downsides than upsides or the use of the wrong approach to address certain problems. Most (a median of 78.40%) reactions on a pull request come before the closing of the corresponding pull requests. Interestingly, we observe that non-contributors (i.e., outsiders who potentially are the “end-users” of the software) are also active on reacting to pull requests. On top of that, we observe that core contributors, peripheral contributors, casual contributors and outsiders have different behaviors when reacting to pull requests. For instance, most core contributors react in the early stages of a pull request, while peripheral contributors, casual contributors and outsiders react around the closing time or, in some cases, after a pull request is merged. Contributors tend to react to the pull request’s source code, while outsiders are more concerned about the impact of the pull request on the end-user experience. Our findings shed light on common patterns of GitHub reactions usage on pull requests and provide taxonomies about the intention of reactors, which can inspire future studies better leverage pull requests’ reactions.

A systematic literature review on source code similarity measurement and clone detection: Techniques, applications, and challenges

Measuring and evaluating source code similarity is a fundamental software engineering activity that embraces a broad range of applications, including but not limited to code recommendation, duplicate code, plagiarism, malware, and smell detection. This paper proposes a systematic literature review and meta-analysis on code similarity measurement and evaluation techniques to shed light on the existing approaches and their characteristics in different applications. We initially found over 10,000 articles by querying four digital libraries and ended up with 136 primary studies in the field. The studies were classified according to their methodology, programming languages, datasets, tools, and applications. A deep investigation reveals 80 software tools, working with eight different techniques on five application domains. Nearly 49% of the tools work on Java programs and 37% support C and C++, while there is no support for many programming languages. A noteworthy point was the existence of 12 datasets related to source code similarity measurement and duplicate codes, of which only eight datasets were publicly accessible. The lack of reliable datasets, empirical evaluations, hybrid methods, and focuses on multi-paradigm languages are the main challenges in the field. Emerging applications of code similarity measurement concentrate on the development phase in addition to the maintenance.

Application of Crowdsourcing in User Experience collection – a case study of Malayalam mobile applications

PurposeThis paper aims to prove the following hypothesis Problem Statement: HYPOTHESIS (1) User Experience collection of mobile applications can be done using the Crowdsourcing mechanism; (2) User Experience collection of mobile applications are influenced by the mindset of Crowdmembers, culture/ethnicity/social background, ease of interface use and rewards, among other factors.Design/methodology/approachThe authors of this paper, did a literature review first to find if Crowdsourcing was applicable and a used method to solve problems in Software Engineering. This helped us to narrow down the application of Crowdsourcing to the Requirements Engineering-Usability (User Experience) collection. User experience collection of two Malayalam language-based mobile applications, AarogyaSetu and BevQ was done as the next step. Incorporating findings from Study I, another study using AarogyaSetu and Manglish was launched as Study II. The results from both cases were consolidated and analyzed. Significant concerns relating to expectations of Crowd members with User Experience collection were unraveled and the purpose of Study was accomplished.Findings(1) Crowdsourcing is and can be used in Software Engineering activities. (2) Crowd members have expectations (motivating factors) of User Interface and other elements that enable them to be an effective contributor. (3) An individual’s environment and mindset (character) are influential in him becoming a contributor in Crowdsourcing. (4) Culture and social practices of a region strongly affects the crowd-participating decision of an individual.Originality/valueThis is purely self-done work. The value of this research work is two-fold. Crowdsourcing is endorsed significant in Software Engineering tasks, especially in User Experience collection of mobile applications. Two, the Crowd service requesters can be careful about designing the questionnaire for Crowdsourcing. They have to be aware and prepared to meet the expectations of the Crowd. This can ensure the active participation of potential contributors. Future researchers can use the results of this work to base their research on similar purposes.

A new, evidence-based, theory for knowledge reuse in security risk analysis

Security risk analysis (SRA) is a key activity in software engineering but requires heavy manual effort. Community knowledge in the form of security patterns or security catalogs can be used to support the identification of threats and security controls. However, no evidence-based theory exists about the effectiveness of security catalogs when used for security risk analysis. We adopt a grounded theory approach to propose a conceptual, revised and refined theory of SRA knowledge reuse. The theory refinement is backed by evidence gathered from conducting interviews with experts (20) and controlled experiments with both experts (15) and novice analysts (18). We conclude the paper by providing insights into the use of catalogs and managerial implications.

Constructing Traceability Links between Software Requirements and Source Code Based on Neural Networks

Software requirement changes, code changes, software reuse, and testing are important activities in software engineering that involve the traceability links between software requirements and code. Software requirement documents, design documents, code documents, and test case documents are the intermediate products of software development. The lack of interrelationship between these documents can make it extremely difficult to change and maintain the software. Frequent requirements and code changes are inevitable in software development. Software reuse, change impact analysis, and testing also require the relationship between software requirements and code. Using these traceability links can improve the efficiency and quality of related software activities. Existing methods for constructing these links need to be better automated and accurate. To address these problems, we propose to embed software requirements and source code into feature vectors containing their semantic information based on four neural networks (NBOW, RNN, CNN, and self-attention). Accurate traceability links from requirements to code are established by comparing the similarity between these vectors. We develop a prototype tool RCT based on this method. These four networks’ performances in constructing links are explored on 18 open-source projects. The experimental results show that the self-attention network performs best, with an average Recall@50 value of 0.687 on the 18 projects, which is higher than the other three neural network models and much higher than previous approaches using information retrieval and machine learning.

Generating of Test Data by Harmony Search Against Genetic Algorithms

Many search-based algorithms have been successfully applied in several software engineering activities. Genetic algorithms (GAs) are the most used in the scientific domains by scholars to solve software testing problems. They imitate the theory of natural selection and evolution. The harmony search algorithm (HSA) is one of the most recent search algorithms in the last years. It imitates the behavior of a musician to find the best harmony. Scholars have estimated the similarities and the differences between genetic algorithms and the harmony search algorithm in diverse research domains. The test data generation process represents a critical task in software validation. Unfortunately, there is no work comparing the performance of genetic algorithms and the harmony search algorithm in the test data generation process. This paper studies the similarities and the differences between genetic algorithms and the harmony search algorithm based on the ability and speed of finding the required test data. The current research performs an empirical comparison of the HSA and the GAs, and then the significance of the results is estimated using the t-Test. The study investigates the efficiency of the harmony search algorithm and the genetic algorithms according to (1) the time performance, (2) the significance of the generated test data, and (3) the adequacy of the generated test data to satisfy a given testing criterion. The results showed that the harmony search algorithm is significantly faster than the genetic algorithms because the t-Test showed that the p-value of the time values is 0.026 < α (α is the significance level = 0.05 at 95% confidence level). In contrast, there is no significant difference between the two algorithms in generating the adequate test data because the t-Test showed that the p-value of the fitness values is 0.25 > α.

Impact in Software Engineering Activities After One Year of COVID-19 Restrictions for Startups and Established Companies

Impact in Software Engineering Activities After One Year of COVID-19 Restrictions for Startups and Established Companies

Events Sourcing and Command Query Responsibility Segregation Based Fast Data Architecture

With the advent of Big Data, the fields of Statistics and Computer Science coexist in current information systems. In addition to this, technological advances in embedded systems, in particular Internet of Things technologies, make it possible to develop real-time applications. These technological developments are disrupting Software Engineering because the use of large amounts of real-time data requires advanced thinking in terms of software architecture. The purpose of this article is to propose an architecture unifying not only Software Engineering and Big Data activities, but also batch and streaming architectures for the exploitation of massive data. This architecture has the advantage of making possible the development of applications and digital services exploiting very large volumes of data in real time; both for management needs and for analytical purposes. This architecture was tested on COVID-19 data as part of the development of an application for real-time monitoring of the evolution of the pandemic in Côte d’Ivoire using PostgreSQL, ELasticsearch, Kafka, Kafka Connect, NiFi, Spark, Node-Red and MoleculerJS to operationalize the architecture.

A Paradigm Shift in Automating Software Engineering Tasks: Bots

Increasing developer productivity and improving software quality are enduring challenges in software engineering. Addressing these challenges has influenced research toward improved automation and formalisms to realize software development tasks, along the way seeking for disruptive ways to support software developers. The past 55 years of software engineering has seen a number of paradigm shifts, fundamental changes in our underlying assumptions which led to making some progress in developing better quality software and disruptive improvements. For example, agile and lean software development approaches challenged our assumptions about the way software development tasks are orchestrated and communicated. Agile and lean software development philosophies advocate elimination of waste by emphasizing value-added tasks and encouraging improved understanding of what tasks stay in inventory.1 Embracing agile and lean software development approaches with discipline led software engineers to prioritize software engineering activities with a bias towards visible delivery, most often leading to improved software quality and developer well being when done right.

Towards cost-benefit evaluation for continuous software engineering activities

Context:Software companies must become better at delivering software to remain relevant in the market. Continuous integration and delivery practices promise to streamline software deliveries to end-users by implementing an automated software development and delivery pipeline. However, implementing or retrofitting an organization with such a pipeline is a substantial investment, while the reporting on benefits and their relevance in specific contexts/domains are vague.Aim:In this study, we explore continuous software engineering practices from an investment-benefit perspective. We identify what benefits can be attained by adopting continuous practices, what the associated investments and risks are, and analyze what parameters determine their relevance.Method:We perform a multiple case study to understand state-of-practice, organizational aims, and challenges in adopting continuous software engineering practices. We compare state-of-practice with state-of-the-art to validate the best practices and identify relevant gaps for further investigation.Results:We found that companies start the CI/CD adoption by automating and streamlining the internal development process with clear and immediate benefits. However, upgrading customers to continuous deliveries is a major obstacle due to existing agreements and customer push-back. Renegotiating existing agreements comes with a risk of losing customers and disrupting the whole organization.Conclusions:We conclude that the benefits of CI/CD are overstated in literature without considering the contextual and domain complexities rendering some benefits infeasible. We identify the need to understand the customer and organizational perspectives further and understand the contextual requirements towards the CI/CD.

Testing research software: a survey

BackgroundResearch software plays an important role in solving real-life problems, empowering scientific innovations, and handling emergency situations. Therefore, the correctness and trustworthiness of research software are of absolute importance. Software testing is an important activity for identifying problematic code and helping to produce high-quality software. However, testing of research software is difficult due to the complexity of the underlying science, relatively unknown results from scientific algorithms, and the culture of the research software community.AimsThe goal of this paper is to better understand current testing practices, identify challenges, and provide recommendations on how to improve the testing process for research software development.MethodWe surveyed members of the research software developer community to collect information regarding their knowledge about and use of software testing in their projects.ResultsWe analysed 120 responses and identified that even though research software developers report they have an average level of knowledge about software testing, they still find it difficult due to the numerous challenges involved. However, there are a number of ways, such as proper training, that can improve the testing process for research software.ConclusionsTesting can be challenging for any type of software. This difficulty is especially present in the development of research software, where software engineering activities are typically given less attention. To produce trustworthy results from research software, there is a need for a culture change so that testing is valued and teams devote appropriate effort to writing and executing tests.

Professional competencies of future software engineers in the software design: teaching techniques

The article is devoted to one of the competence components of a mobile-oriented environment for professional and practical training of future software engineers. It is shown that the introduction of higher education standard 121 “Software Engineering” for the first (bachelor) level of higher education in Ukraine has generated a number of training quality assurance problems associated primarily with the low level of detailed competencies and program learning outcomes. By solving these problems, the detailed design of the system of professional competencies for future software engineers is developed. The article deals with the approaches to developing one of the most important special professional competences of future software engineers – the ability to participate in software design, including modeling (formal description) of its structure, behavior, and processes of functioning. Based on a historical and genetic review of the software engineering training practice of future software engineers in the USA, UK, Canada, Australia, New Zealand and Singapore, recommendations for choosing forms of training organization, selection of training content, ways of students’ and teachers’ activities in software engineering, modeling and designing tools; assessment of the appropriate competence formation level are formulated. The example of organizing design training in conditions close to industrial-studio training is considered. The problems of transition from architectural to detailed design and project implementation are shown. Prospects for further development of this study are to substantiate the third (after requirements engineering and design engineering) engineering component of software engineering – the software construction.

The MESSIR Flexible Scientific Approach to Requirements Engineering

Among the software engineering development phases, requirements engineering is the one that has the most impact on project success or failure. To be executed in various contexts, there is an important need for flexibility and efficient tool support. A flexible requirements engineering method should include several levels allowing for more or less completeness and precision. Some project contexts would need a lightweight activity using structured natural language but still being guided and grounded partly on professional standards. Some more advanced projects would need more complete requirements documents and would benefit from a description language based on scientific notions allowing for better precision for specific system operations. Some business or safety critical systems would need an approach allowing for requirements simulation and verification. Requirements engineering education is an important objective to prepare future engineers to understand those requirements engineering needs and be prepared for practice in a professional setting. In the last five years, we have developed a requirements engineering method called Messir with a tool Excalibur and experiments in academia have been made to see how it was solving actual software engineering problems focusing first on requirements engineering education. Messir components represent in themselves some improvements w.r.t. the state of the art of the “standard” theories, methods and tools, mainly by introducing an improved requirements engineering process, language and verification support based on executable requirements specifications. Furthermore, the Messir approach solves also some actual problems related to software engineering education by offering a product line framework for setting up or improving courses in computer science curricula. The main result being to contribute to develop the software engineering capabilities of engineers and scientists that feed the job market in industry, research or education.

A study on the use of vulnerabilities databases in software engineering domain

Over the last decade several software vulnerability databases have been introduced to guide researchers and developers in developing more secure and reliable software. While the Software Engineering research community is increasingly becoming aware of these vulnerabilities databases, no comprehensive literature survey exists that studies how they are used in software development. The objective of our survey is to provide insights on how the software vulnerability database (SVDBs) research landscape has evolved over the past 17 years and outline some open challenges associated with their use in non-security domain. More specifically, we introduce a semi-automated methodology based on topic modeling, to discover relevant topics from our dataset of 99 relevant SE research articles. We find 24 topics discussing the use of SVDBs in SE domain. The results shows that i) topics describing the use of SVDBs range from security empirical (case) studies to tools for generating security test cases; ii) the majority of the surveyed papers cover a limited number of software engineering contributions or activities (e.g., maintenance) and iii) that most of the surveyed articles rely on only one SVDB as their knowledge source. Dataset and results are available at https://github.com/isultane/svdbs_dataset

Leveraging Social Network Analysis for Crowdsourced Software Engineering Research

Crowdsourced software engineering (CSE) is an emerging area that has been gaining much attention in the last few years. It refers to the use of crowdsourcing techniques in software engineering activities, including requirements engineering, implementation, design, testing, and verification. CSE is an alternative to traditional software engineering and uses an open call to which online developers can respond to and obtain work on various tasks, as opposed to the assigning of tasks to in-house developers. The great benefits of CSE have attracted the attention of many researchers, and many studies have recently been carried out in the field. This research aims to analyze publications on CSE using social network analysis (SNA). A total of 509 CSE publications from six popular databases were analyzed to determine the characteristics of the collaborative networks of co-authorship of the research (i.e., the co-authors, institutions involved in co-authorship, and countries involved in co-authorship) and of the citation networks on which the publications of the studies are listed. The findings help identify CSE research productivity, trends, performances, community structures, and relationships between various collaborative patterns to provide a more complete picture of CSE research.

Extracting Formats of Service Messages with Varying Payloads

Having precise specifications of service APIs is essential for many Software Engineering activities. Unfortunately, available documentation of services is often inadequate and/or imprecise and, hence, cannot be fully relied upon. Generating service documentation manually is a tedious and error-prone task, especially in light of changes to services. Therefore, there is a need for automated support in generating service documentation. In this work, we present a novel approach to infer the API of a service by analyzing recorded messages sent to and received from this service. Our approach includes a novel, two-level clustering technique to cluster messages, a step that many existing approaches to infer message formats fail to perform precisely in the presence of significant variation of payload information of the available messages. We have evaluated our approach on message traces from four different real-world services. The experimental result shows that our approach is more effective than existing techniques in extracting correct message formats from recorded messages.

Cognition in Software Engineering: A Taxonomy and Survey of a Half-Century of Research

Cognition plays a fundamental role in most software engineering activities. This article provides a taxonomy of cognitive concepts and a survey of the literature since the beginning of the Software Engineering discipline. The taxonomy comprises the top-level concepts of perception, attention, memory, cognitive load, reasoning, cognitive biases, knowledge, social cognition, cognitive control, and errors, and procedures to assess them both qualitatively and quantitatively. The taxonomy provides a useful tool to filter existing studies, classify new studies, and support researchers in getting familiar with a (sub) area. In the literature survey, we systematically collected and analysed 311 scientific papers spanning five decades and classified them using the cognitive concepts from the taxonomy. Our analysis shows that the most developed areas of research correspond to the four life-cycle stages, software requirements, design, construction, and maintenance. Most research is quantitative and focuses on knowledge, cognitive load, memory, and reasoning. Overall, the state of the art appears fragmented when viewed from the perspective of cognition. There is a lack of use of cognitive concepts that would represent a coherent picture of the cognitive processes active in specific tasks. Accordingly, we discuss the research gap in each cognitive concept and provide recommendations for future research.

TORACLE 2021 Workshop Summary

Testing is an important activity in software engineering, especially with the growing adoption of software systems in safety-critical domains. Testing research, however, is mostly focused on deter- mining which test inputs to use (e.g., proposing and evaluating test coverage criteria or automatic test generation tools). Re- gardless of the used coverage criterion, we need to know whether a given program executes correctly on a given input. Indeed, a test execution for which we cannot discriminate between success or failure serves little purpose. This corresponds to the so-called øracle problem", the problem of knowing whether a program be- haves correctly for a speci c input. Although the importance of the oracle problem is well understood, only a few alternatives exist to manually deriving test oracles. This makes automated test or- acle generation one of the main bottlenecks for achieving full test automation. Therefore, novel approaches and tools are needed to address this important problem.

Application of Hash Sha-256 Algorithm in Website-Based Sales Software Engineering

Rapid technological developments can spur changes in the cycle of human activities, one of which is software engineering activities that continue to develop in accordance with technological developments, the development of software engineering activities is also developing methods of data security to withstand attacks from irresponsible parties. answer. This research was conducted to analyze the performance and robustness of the sha-256 data security method with ciphertext customization. The steps that the researchers took in conducting the analysis were collecting theory and case examples, designing programs, implementing programs, testing and saving the results. Based on this process, it can be concluded that ciphertext customization on sha-256 is needed to strengthen security and resistance to attacks from irresponsible parties, besides that the performance of sha-256 calculations with customization on ciphertext and without ciphertext is not too much different where only 65 ms difference based on the results of performance testing that researchers did.

Analysing app reviews for software engineering: a systematic literature review

App reviews found in app stores can provide critically valuable information to help software engineers understand user requirements and to design, debug, and evolve software products. Over the last ten years, a vast amount of research has been produced to study what useful information might be found in app reviews, and how to mine and organise such information as efficiently as possible. This paper presents a comprehensive survey of this research, covering 182 papers published between 2012 and 2020. This survey classifies app review analysis not only in terms of mined information and applied data mining techniques but also, and most importantly, in terms of supported software engineering activities. The survey also reports on the quality and results of empirical evaluation of existing techniques and identifies important avenues for further research. This survey can be of interest to researchers and commercial organisations developing app review analysis techniques and to software engineers considering to use app review analysis.