Software Engineering Research Research Articles

Supporting Emotional Intelligence, Productivity and Team Goals while Handling Software Requirements Changes

Background: Research shows that emotional intelligence (EI) should be used alongside cognitive intelligence during requirements change (RC) handling in Software Engineering (SE), especially in agile settings. Objective: We wanted to study the role of EI in-depth during RC handling. Method: We conducted a mixed-methods study (an interview study followed by a survey study) with 124 software practitioners. Findings: We found the causal condition, intervening condition and causes lead to key direct consequences of regulating own emotions, managing relationships, and extended consequences of sustaining productivity, setting and sustaining team goals. We found several strategies of supporting EI during RC handling. Further, we found strong correlations between six strategies and one being aware of own emotions, regulating own emotions, sustaining team productivity, and setting and sustaining team goals. Conclusion: Empathising with others and tracking commitments and decisions as a team are key strategies that have strong correlations between managing emotions, between sustaining team productivity, and between setting and sustaining team goals. To the best of our knowledge, the framework we present in this paper is the first theoretical framework on EI in SE research. We provide recommendations for software practitioners to consider during RC handling.

Thoughts on applicability

The gulf between industry and academic interests in Software Engineering appears to continue to widen. With software taking on an ever-growing role in society, it is somewhat concerning that the interests and research focuses of academic software engineers seem to rarely overlap with industry interests. In this editorial, I hypothesize four major concerns I have with the applicability of much of the research in this area. I also surface the important meta question: What is Software Engineering Research? Is this the study of software engineers, or the study of best practices in software engineering? Does academic SE research owe more to anthropology, or the study of what makes software successful?

A Meta-Study of Software-Change Intentions

Every software system undergoes changes, for example, to add new features, fix bugs, or refactor code. The importance of understanding software changes has been widely recognized, resulting in various techniques and studies, for instance, on change-impact analysis or classifying developers’ activities. Since changes are triggered by developers’ intentions—something they plan or want to change in the system, many researchers have studied intentions behind changes. While there appears to be a consensus among software-engineering researchers and practitioners that knowing the intentions behind software changes is important, it is not clear how developers can actually benefit from this knowledge. In fact, there is no consolidated, recent overview of the state-of-the-art on software-change intentions (SCIs) and their relevance for software engineering. We present a meta-study of 122 publications, which we used to derive a categorization of SCIs; and to discuss motivations, evidence, and techniques relating to SCIs. Unfortunately, we found that individual pieces of research are often disconnected from each other because a common understanding is missing. Similarly, some publications showcase the potential of knowing SCIs, but more substantial research to understand the practical benefits of knowing SCIs is needed. Our contributions can help researchers and practitioners improve their understanding of SCIs and how SCIs can aid software engineering tasks.

A Survey of Source Code Search: A 3-Dimensional Perspective

(Source) code search is widely concerned by software engineering researchers because it can improve the productivity and quality of software development. Given a functionality requirement usually described in a natural language sentence, a code search system can retrieve code snippets that satisfy the requirement from a large-scale code corpus, e.g., GitHub. To realize effective and efficient code search, many techniques have been proposed successively. These techniques improve code search performance mainly by optimizing three core components, including query understanding component, code understanding component, and query-code matching component. In this paper, we provide a 3-dimensional perspective survey for code search. Specifically, we categorize existing code search studies into query-end optimization techniques, code-end optimization techniques, and match-end optimization techniques according to the specific components they optimize. These optimization techniques are proposed to enhance the performance of specific components, and thus the overall performance of code search. Considering that each end can be optimized independently and contributes to the code search performance, we treat each end as a dimension. Therefore, this survey is 3-dimensional in nature, and it provides a comprehensive summary of each dimension in detail. To understand the research trends of the three dimensions in existing code search studies, we systematically review 68 relevant literatures. Different from existing code search surveys that only focus on the query end or code end or introduce various aspects shallowly (including codebase, evaluation metrics, modeling technique, etc.), our survey provides a more nuanced analysis and review of the evolution and development of the underlying techniques used in the three ends. Based on a systematic review and summary of existing work, we outline several open challenges and opportunities at the three ends that remain to be addressed in future work.

Academic Freedom and International Students, Part 5: The gray areas

Computer science and software engineering students and researchers are busy with their courses, in their labs, working on their studies and research - for the most part not really thinking about the impact of politics on their research. However, there are increasing restrictions on the information researchers are allowed to share with their students, and who researchers are allowed to collaborate with respect to national security and increasing concerns with China. To an increasing extent researchers are made aware at grant time. Research institutions must follow federal rules in the grant and hiring process and are conducting workshops and roundtables on issues of national security. The AIP, The American Institute of Physics provides through their mailing list an informative brief on national science policy titled "FYI: Science Policy News from AIP, This Week." The issue for January 23, 2023, invited those interested to join a National Academies of Science, Engineering, Medicine workshop called a "Meeting of experts," on the topic of "Fundamental Research, Openness, and Protecting the U.S. Technological Advantage: NSDD-189 in the New Global Context."

Taxonomy of inline code comment smells

Code comments play a vital role in source code comprehension and software maintainability. It is common for developers to write comments to explain a code snippet, and commenting code is generally considered a good practice in software engineering. However, low-quality comments can have a detrimental effect on software quality or be ineffective for code understanding. This study aims to create a taxonomy of inline code comment smells and determine how frequently each smell type occurs in software projects. We conducted a multivocal literature review to define the initial taxonomy of inline comment smells. Afterward, we manually labeled 2447 inline comments from eight open-source projects where half of them were Java, and another half were Python projects. We created a taxonomy of 11 inline code comment smell types and found out that the smells exist in both Java and Python projects with varying degrees. Moreover, we conducted an online survey with 41 software practitioners to learn their opinions on these smells and their impact on code comprehension and software maintainability. The survey respondents generally agreed with the taxonomy; however, they reported that some smell types might have a positive effect on code comprehension in certain scenarios. We also opened pull requests and issues fixing the comment smells in the sampled projects, where we got a 27% acceptance rate. We share our manually labeled dataset online and provide implications for software engineering practitioners, researchers, and educators.

Women and Software Engineering

My goal with this text is to provide some concepts and questions that can help the reader to reflect on and elevate the discussion about Women and Software Engineering by providing historical data and some reflection points for the future. Feminism constitutes both a theoretical perspective and a social movement aiming to diminish and ultimately eliminate gender-based inequality and oppression. Data feminism merges data science with feminist principles to examine and address biases and power dynamics in data and technology. Studies at the intersection between gender and software engineering cover gender representation, barriers, and experiences. The main questions I propose for future reflections in the community are: What are the benefits that women bring to software engineering? How does the career and the life of female software engineers unfold? How should software engineering research change so that feminist principles are incorporated? How can feminist knowledge and processes help to examine power structure in software engineering?

On the accessibility of the grayscale version of a PoN‐enabled use case notation

AbstractThe physics of notations (PoN) is a framework for designing cognitively effective notations. Cognitive effectiveness refers to the ease (speed and accuracy) by which model readers read models. In 2019, a PoN‐Enabled notation for use case diagrams was proposed. It has been empirically proven that the cognitive effectiveness of this new notation is superior to the original use case notation. The new notation however relies in part on the use of color. The accessibility of this new design has not been validated for use by the colorblind community, or any users who need to use a grayscale version of the models created. The PoN framework was not designed to explicitly account for the colorblind community or users who can only access grayscale versions of models. Inclusivity should not be an afterthought in software engineering practice or research. To this end, this paper empirically evaluates the cognitive effectiveness of the PoN‐enabled use case notation in situations where it is viewed in grayscale.

Security for Machine Learning-based Software Systems: A Survey of Threats, Practices, and Challenges

The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision and video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, how to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge, for which the insufficient consideration will largely limit its application in safety-critical domains. One concern is that the present MLBSS development tends to be rushed, and the latent vulnerabilities and privacy issues exposed to external users and attackers will be largely neglected and hard to be identified. Additionally, machine learning-based software systems exhibit different liabilities towards novel vulnerabilities at different development stages from requirement analysis to system maintenance, due to its inherent limitations from the model and data and the external adversary capabilities. The successful generation of such intelligent systems will thus solicit dedicated efforts jointly from different research areas, i.e., software engineering, system security, and machine learning. Most of the recent works regarding the security issues for ML have a strong focus on the data and models, which has brought adversarial attacks into consideration. In this work, we consider that security for machine learning-based software systems may arise from inherent system defects or external adversarial attacks, and the secure development practices should be taken throughout the whole lifecycle. While machine learning has become a new threat domain for existing software engineering practices, there is no such review work covering the topic. Overall, we present a holistic review regarding the security for MLBSS, which covers a systematic understanding from a structure review of three distinct aspects in terms of security threats. Moreover, it provides a thorough state-of-the-practice for MLBSS secure development. Finally, we summarize the literature for system security assurance and motivate the future research directions with open challenges. We anticipate this work provides sufficient discussion and novel insights to incorporate system security engineering for future exploration.

Ethics: What is the Brazilian Software Engineering Research Scenario?

Background: Ethics is the theory or science of the moral behavior of humans in society. Traditionally, we value “unethical” actions that go against determining morality in a specific context. One of the sub-domains of Ethics is Computational Ethics, which deals with ethical dilemmas that are strictly related to computational issues. Dilemmas in this area involve privacy, improper access, intellectual property, digital norms and laws, power, socio-technical aspects (such as gender discrimination), and robotics, among others. In this context, “Software Engineering” and “Software” are different objects. Engineering is an act, a practice, as also coding, programming, and software reuse. As with any act, moral and subsequent ethical considerations are appropriate. We characterize software as an object of concrete reality, as a sociotechnical system formed by a technical artifact, human aspect, and procedural aspect. This assumption will form the main base discussion of ethics and morals in Software Engineering in this paper. Objective: The goal of this paper is to unveil the Brazilian Software Engineering ethics panorama. Method: We follow the rigor of, and inspired by, a Systematic Literature Review (SLR) protocol to answer the question: how does ethics explicitly permeate the Brazilian Software Engineering publications between the last thirteen years (2010 and 2022)? Results: After analyzing 1529 papers through the research protocol, 175 (≈11%) presented some explicit occurrence of ethical aspects. The occurrence was relevant in only 7 papers (≈0.4%), exposing a shallow scenario on ethical or moral aspects. Conclusions: If Ethics is a topic considered important to deliberate, research or discuss, this did not occur significantly in the Brazilian Software Engineering research scenario since 2010. With this result in mind, we discussed parallel terms and concepts to enrich the contribution of the qualitative synthesis.

Enablers and Barriers of Empathy in Software Developer and User Interactions: A Mixed Methods Case Study

Software engineering (SE) requires developers to collaborate with stakeholders, and understanding their emotions and perspectives is often vital. Empathy is a concept characterising a person’s ability to understand and share the feelings of another. However, empathy continues to be an under-researched human aspect in SE. We studied how empathy is practised between developers and end users using a mixed methods case study. We used an empathy test, observations and interviews to collect data, and socio–technical grounded theory and descriptive statistics to analyse data. We identified the nature of awareness required to trigger empathy and enablers of empathy. We discovered barriers to empathy and a set of potential strategies to overcome these barriers. We report insights on emerging relationships and present a set of recommendations and potential future works on empathy and SE for software practitioners and SE researchers.

Can AI serve as a substitute for human subjects in software engineering research?

Can AI serve as a substitute for human subjects in software engineering research?

Test Code Flakiness in Mobile Apps: The Developer’s Perspective

Context:Test flakiness arises when test cases have a non-deterministic, intermittent behavior that leads them to either pass or fail when run against the same code. While researchers have been contributing to the detection, classification, and removal of flaky tests with several empirical studies and automated techniques, little is known about how the problem of test flakiness arises in mobile applications. Objective:We point out a lack of knowledge on: (1) The prominence and harmfulness of the problem; (2) The most frequent root causes inducing flakiness; and (3) The strategies applied by practitioners to deal with it in practice. An improved understanding of these matters may lead the software engineering research community to assess the need for tailoring existing instruments to the mobile context or for brand-new approaches that focus on the peculiarities identified. Methods:We address this gap of knowledge by means of an empirical study into the mobile developer’s perception of test flakiness. We first perform a systematic grey literature review to elicit how developers discuss and deal with the problem of test flakiness in the wild. Then, we complement the systematic review through a survey study that involves 130 mobile developers and that aims at analyzing their experience on the matter. Results:The results of the grey literature review indicate that developers are often concerned with flakiness connected to user interface elements. In addition, our survey study reveals that flaky tests are perceived as critical by mobile developers, who pointed out major production code- and source code design-related root causes of flakiness, other than the long-term effects of recurrent flaky tests. Furthermore, our study lets the diagnosing and fixing processes currently adopted by developers and their limitations emerge. Conclusion:We conclude by distilling lessons learned, implications, and future research directions.

Ethical Aspects of ChatGPT in Software Engineering Research

Ethical Aspects of ChatGPT in Software Engineering Research

Large language models for qualitative research in software engineering: exploring opportunities and challenges

Large language models for qualitative research in software engineering: exploring opportunities and challenges

Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice

Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents to software engineering researchers the six key challenges that a domain expert faces in addressing their problem with a computational workflow, and the underlying executable implementation. These challenges arise out of our conceptual framework which presents the “route” of transformations that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state-of-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for the transformations described in our framework. Example studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation. The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges and transformations are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.

Summary of the 1st Interpretability and Robustness in Neural Software Engineering (InteNSE 2023)

InteNSE is an interdisciplinary workshop for research at the intersection of Machine Learning (ML) and Software Engineering (SE) and would be a pioneer in emphasizing the implicit properties of neural software engineering and analysis. Due to recent computational advancements, ML has become an inseparable part of the SE research community. ML can indeed improve and revolutionize many SE tasks. However, most research in the AI and SE communities consider ML as a closed box, i.e., only considering the final performance of the developed models as an evaluation metric. Ignoring the implicit properties of neural models, such as interpretability and robustness, one cannot validate the model's actual performance, generalizability, and whether it is learning what it is supposed to do. Specifically, in the domain of SE, where the result of ML4SE tools is code synthesis, bug finding, or repair, interpretability and robustness are crucial to ensure the reliability of the products.

Using Voice and Biofeedback to Predict User Engagement during Product Feedback Interviews

Capturing users’ engagement is crucial for gathering feedback about the features of a software product. In a market-driven context, current approaches to collecting and analyzing users’ feedback are based on techniques leveraging information extracted from product reviews and social media. These approaches are hardly applicable in contexts where online feedback is limited, as for the majority of apps, and software in general. In such cases, companies need to resort to face-to-face interviews to get feedback on their products. In this paper, we propose to utilize biometric data, in terms of physiological and voice features, to complement product feedback interviews with information about the engagement of the user on product-relevant topics. We evaluate our approach by interviewing users while gathering their physiological data (i.e., biofeedback ) using an Empatica E4 wristband, and capturing their voice through the default audio-recorder of a common laptop. Our results show that we can predict users’ engagement by training supervised machine learning algorithms on biofeedback and voice data, and that voice features alone can be sufficiently effective. The best configurations evaluated achieve an average F1 \(\sim 70\% \) in terms of classification performance, and use voice features only. This work is one of the first studies in requirements engineering in which biometrics are used to identify emotions. Furthermore, this is one of the first studies in software engineering that considers voice analysis. The usage of voice features can be particularly helpful for emotion-aware feedback collection in remote communication, either performed by human analysts or voice-based chatbots, and can also be exploited to support the analysis of meetings in software engineering research.

Threats to validity in software engineering research: A critical reflection

Threats to validity in software engineering research: A critical reflection

An Observational Study on React Native (RN) Questions on Stack Overflow (SO)

Mobile applications are continuously increasing in prevalence. One of the main challenges in mobile application development is creating cross-platform applications. To facilitate developing cross-platform applications, the software engineering community created several solutions, one of which is React Native (RN), which is a popular cross-platform framework. The software engineering literature demonstrated the effectiveness of Stack Overflow (SO) in providing real-world perspectives on a variety of technical subjects. Therefore, this study aims to gain a better understanding of the stance of RN on SO. We identified and analyzed 131,620 SO RN-related questions. Moreover, we observed how the interest toward RN on SO evolves over time. Additionally, we utilized Latent Dirichlet Allocation (LDA) to identify RN-related topics that are discussed within the questions. Afterward, we utilized a number of proxy measures to estimate the popularity and difficulty of these topics. The results revealed that interest toward RN on SO was generally increasing. Moreover, RN-related questions revolve around six topics, with the topics of layout and navigation being the most popular and the topic of iOS issues being the most difficult. Software engineering researchers, practitioners, educators, and RN contributors may find the results of this study beneficial in guiding their future RN efforts.