Context Of Software Engineering Research Articles

Qualitative software engineering research: Reflections and guidelines

AbstractResearchers are increasingly recognizing the importance of human aspects in software development. Because qualitative methods are used to explore human behavior in‐depth, we believe that studies using such methods will become more common. Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and the knowledge on how to use them like in social sciences. The purpose of this study was to extend the software engineering community's current body of knowledge regarding available qualitative methods and their quality assurance frameworks and to provide recommendations and guidelines for their use. With the support of an epistemological argument and a survey of the literature, we suggest that future research would benefit from (1) utilizing a broader set of research methods, (2) more strongly emphasizing reflexivity, and (3) employing qualitative guidelines and quality criteria. We present an overview of three qualitative methods commonly used in social sciences but rarely seen in software engineering research, namely interpretative phenomenological analysis, narrative analysis, and discourse analysis. Furthermore, we discuss the meaning of reflexivity in relation to the software engineering context and suggest means of fostering it. Our paper will help software engineering researchers better select and then guide the application of a broader set of qualitative research methods.

Empathy models and software engineering — A preliminary analysis and taxonomy

Empathy is widely used in many disciplines such as philosophy, sociology, psychology, health care. Ability to empathise with software end-users seems to be a vital skill software developers should possess. This is because engineering successful software systems involves not only interacting effectively with users but also understanding their true needs. Empathy has the potential to address this situation. Empathy is a predominant human aspect that can be used to comprehend decisions, feelings, emotions and actions of users. However, to date empathy has been under-researched in software engineering (SE) context. In this position paper, we present our exploration of key empathy models from different disciplines and our analysis of their adequacy for application in SE. While there is no evidence for empathy models that are readily applicable to SE, we believe these models can be adapted and applied in SE context with the aim of assisting software engineers to increase their empathy for diverse end-user needs. We present a preliminary taxonomy of empathy by carefully considering the most popular empathy models from different disciplines. We encourage future research on empathy in SE as we believe it is an important human aspect that can significantly influence the relationship between developers and end-users.

On the Validity of Pre-Trained Transformers for Natural Language Processing in the Software Engineering Domain

Transformers are the current state-of-the-art of natural language processing in many domains and are using traction within software engineering research as well. Such models are pre-trained on large amounts of data, usually from the general domain. However, we only have a limited understanding regarding the validity of transformers within the software engineering domain, i.e., how good such models are at understanding words and sentences within a software engineering context and how this improves the state-of-the-art. Within this article, we shed light on this complex, but crucial issue. We compare BERT transformer models trained with software engineering data with transformers based on general domain data in multiple dimensions: their vocabulary, their ability to understand which words are missing, and their performance in classification tasks. Our results show that for tasks that require understanding of the software engineering context, pre-training with software engineering data is valuable, while general domain models are sufficient for general language understanding, also within the software engineering domain.

Software Engineering Education: Discovering Future Skills

In the contemporary digital age the majority of industries become very much software dependent, require automation, digitization, and deployment of new technologies. The need for specialists, skilled in software engineering, is in high demand. In this study, we set out to investigate what challenges may arise in IT students skill sets and new competences in the software engineering context and answer the question how is skills demand evolving and where the gaps will be?". The paper analyzes what specific knowledge needs to be developed during university education and identifies their evolving skills demand. The paper recommends how students and young specialists in software engineering need to change themselves to adapt to the changing world.

The effects of applying filters on EEG signals for classifying developers’ code comprehension

EEG signals are a relevant indicator for measuring aspects related to human factors in Software Engineering. EEG is used in software engineering to train machine learning techniques for a wide range of applications, including classifying task difficulty, and developers’ level of experience. The EEG signal contains noise such as abnormal readings, electrical interference, and eye movements, which are usually not of interest to the analysis, and therefore contribute to the lack of precision of the machine learning techniques. However, research in software engineering has not evidenced the effectiveness when applying these filters on EEG signals. The objective of this work is to analyze the effectiveness of filters on EEG signals in the software engineering context. As literature did not focus on the classification of developers’ code comprehension, this study focuses on the analysis of the effectiveness of applying EEG filters for training a machine learning technique to classify developers' code comprehension. A Random Forest (RF) machine learning technique was trained with filtered EEG signals to classify the developers' code comprehension. This study also trained another random forest classifier with unfiltered EEG data. Both models were trained using 10-fold cross-validation. This work measures the classifiers' effectiveness using the f-measure metric. This work used the t-test, Wilcoxon, and U Mann Whitney to analyze the difference in the effectiveness measures (f-measure) between the classifier trained with filtered EEG and the classifier trained with unfiltered EEG. The tests pointed out that there is a significant difference after applying EEG filters to classify developers' code comprehension with the random forest classifier. The conclusion is that the use of EEG filters significantly improves the effectivity to classify code comprehension using the random forest technique.

Product management and the construction of the required software

This article describes an applied research conducted in a technology company. The project addresses the adoption of product management within the context of software engineering, analyzing changes in environment and work dynamics resulting from this adoption. The goal is to understand how the management of software products in the Agreements Module is transforming the software engineering of a technology company, working on the uncertainties pertinent to the product at the implementation phase, avoiding the mismatch of information that ends up causing the non-delivery of the products, failing customer requirements, and creating unrealistic schedules and budgets. The purpose of this work is to contribute to the area of technology by producing content for a branch not yet explored in academia, as it is experiencing great growth in the labor market. The cohort of this research was ten professionals from Agreements squad: five developers, one design analyst, one quality analyst, one product manager, one agile coach and one coordinator. Through surveys applied to the end user and to the team involved at the development, it was possible to assess the level of satisfaction within the process and the product delivered. Throughout the article, it is possible to observe the importance of product management in the production of software; as when implemented in a planned and integrated way, it can bring profits and avoid rework.

Design of a gamification strategy to intervene in social and human factors associated with software process improvement change resistance

Best practices of software development and software process improvement (SPI) are people-centred. Both have a high level of influence of social and human factors inherent to the individuals involved in these processes. At the same time, software process improvement is inherent to the change of the conditions and the behaviour of individuals. In this work, the authors show the results of applying a deductive approach for a method based on gamification and social and human factors to influence the productivity of the software development team. The results show that it is possible to abstract all the design aspects of an existing strategy called Interplanetary Mission and identify the social and human factors involved in change resistance in SPI initiatives. The strategy execution results show that gamification strategies could help to mitigate change resistance in SPI initiatives for software development teams and their productivity, understanding this term in software engineering context as the ratio between output and input within the software development production process.

Using i* and UML for Blockchain Oriented Software Engineering: Strengths,Weaknesses, Lacks and Complementarity

New blockchain-based projects do appear every day. The technology has indeed been popularized by cryptocurrencies but is now gaining interest in various domains and new types of applications are evaluated constantly. Understanding the impact of blockchain adoption on the organization and the internals of blockchain-related behavior nevertheless remains a challenge for managers but also for IT professionals. This article studies how two existing organizational and software modeling languages can be fit to document a blockchain development project in Supply Chain Management (SCM) at its earliest stages. These two frameworks are i* on the one side and the Unified Modeling Language (UML) use case and sequence diagrams on the other side. The real life project used as a case study in this application is ‘Farm-to-Fork’ where a blockchain solution for the Supply Chain (SC) of farm animals is developed. The application of the frameworks is intended to identify their strengths and weaknesses. An extension of i* is proposed to deal with blockchain privacy issues as well as laws and norms. We finally point to the complementarity of i* and UML use case and sequence diagrams in a Blockchain-Oriented Software Engineering (BOSE) context. The i* framework indeed supports early requirements to understand the impact of the project on stakeholders while UML use case and sequence diagrams support the late requirements and the design by depicting the use of blockchain and some of its behavioral mechanisms.

A Method to Assess and Argue for Practical Significance in Software Engineering

A key goal of empirical research in software engineering is to assess practical significance, which answers the question whether the observed effects of some compared treatments show a relevant difference in practice in realistic scenarios. Even though plenty of standard techniques exist to assess statistical significance, connecting it to practical significance is not straightforward or routinely done; indeed, only a few empirical studies in software engineering assess practical significance in a principled and systematic way. In this paper, we argue that Bayesian data analysis provides suitable tools to assess practical significance rigorously. We demonstrate our claims in a case study comparing different test techniques. The case study's data was previously analyzed (Afzal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2015) using standard techniques focusing on statistical significance. Here, we build a multilevel model of the same data, which we fit and validate using Bayesian techniques. Our method is to apply cumulative prospect theory on top of the statistical model to quantitatively connect our statistical analysis output to a practically meaningful context. This is then the basis both for assessing and arguing for practical significance. Our study demonstrates that Bayesian analysis provides a technically rigorous yet practical framework for empirical software engineering. A substantial side effect is that any uncertainty in the underlying data will be propagated through the statistical model, and its effects on practical significance are made clear. Thus, in combination with cumulative prospect theory, Bayesian analysis supports seamlessly assessing practical significance in an empirical software engineering context, thus potentially clarifying and extending the relevance of research for practitioners.

Management of non-technological projects by embracing agile methodologies

This study aims to address an emerging area of application of agile methodologies outside the traditional technological environment, particularly outside the context of software engineering. In this sense, it intends to understand and contextualise the problems and challenges that are posed by traditional methodologies and explore how agile methodologies can complement or substitute to traditional methodologies in non-technological environments. For this purpose, a qualitative study was performed through three case studies operating in different sectors of activity like digital marketing, management consulting, and tourism. The findings indicate that both traditional and agile methodologies can coexist, and it is common to find hybrid methodologies that are adopted and customised according to the needs of each project and client. Furthermore, it was possible to conclude that following agile practices that make sense to the cultural values of the organisation is more important than adopting a specific agile methodology.

Management of non-technological projects by embracing agile methodologies

This study aims to address an emerging area of application of agile methodologies outside the traditional technological environment, particularly outside the context of software engineering. In this sense, it intends to understand and contextualise the problems and challenges that are posed by traditional methodologies and explore how agile methodologies can complement or substitute to traditional methodologies in non-technological environments. For this purpose, a qualitative study was performed through three case studies operating in different sectors of activity like digital marketing, management consulting, and tourism. The findings indicate that both traditional and agile methodologies can coexist, and it is common to find hybrid methodologies that are adopted and customised according to the needs of each project and client. Furthermore, it was possible to conclude that following agile practices that make sense to the cultural values of the organisation is more important than adopting a specific agile methodology.

The Teamwork Process Antecedents (TPA) questionnaire: developing and validating a comprehensive measure for assessing antecedents of teamwork process quality

Most models of teamwork describe team behavior and effectiveness using an Input-Process-Output approach. In software engineering, the use of such models has focused on understanding and operationalizing the Process-Output components while less research effort has been applied to define and measure the Input-Process component. To develop and validate a measure of teamwork process antecedents (inputs) that addresses specific characteristics of software teams in industrial practice. First, we reviewed the group work literature, identified and integrated previously described antecedents of work group process, and developed a measure to tap those antecedents. This measure is operationalized in the Teamwork Process Antecedents (TPA) questionnaire, which we then validated with 375 Brazilian software engineers from 100 companies, using exploratory and confirmatory factor analysis. We created a survey to operationalize two multidimensional antecedents of teamwork process, Team Structure and Team Composition, based on well-established models from the literature on work teams. We tailored the response items to the software engineering context to increase construct face validity. We reached a parsimonious set of five dimensions for Team Composition (16 response items) and four dimensions for Team Structure (11 response items). Our results show that our measure of TPA has excellent internal reliability and convergent and discriminant validity. We created a novel measure of antecedents of teamwork process tailored to software teams, that captures the perception of team members about the adequacy of team composition and structure to achieve team goals. Further, we present the development of the TPA measure in the form of a guideline that may be used in the construction of other measurement instruments in empirical software engineering research. We believe both results are important contributions of this work.

Motivation and Satisfaction of Software Engineers

Context : The proper management of people can help software organisations to achieve higher levels of success. However, the limited attention paid to the appropriate use of theories to underpin the research in this area leaves it unclear how to deal with human aspects of software engineers, such as motivation and satisfaction. Objectives : This article aims to expose what drives the motivation and satisfaction of software engineers at work. Methods : A multiple case study was conducted at four software organisations in Brazil. For 11 months, data was collected using semi-structured interviews, diary studies, and document analyses. Results : The Theory of Motivation and Satisfaction of Software Engineers (TMS-SE), presented in this article, combines elements from well established theories with new findings, and translates them into the software engineering context. Conclusion : The TMS-SE advances the understanding of people management in the software engineering field and presents a strong conceptual framework for future investigations in this area.

A Strategy to Support Replications of Controlled Experiments in Software Engineering

Replication is essential to build knowledge in empirical science. Experiment replications reported in the software engineering context present variabilities on their experiment elements, e.g., variables, materials. Further understanding these variabilities could help planning lack of strategy to support the representation of experiment variabilities and commonalities. In addition, there is also a gap related to effective reuse and traceability of experiment elements. These problems are likely to hamper the replication understanding and planning. In order to overcome these gaps, we intend to create a conceptual model and a tool to support replication planning. To develop these solutions, we will use concepts of experimentation and software product lines. Our idea is to build a core structure which allows the configuration of experiment elements based commonalities with previous replications and desired variabilities to fit the specific replication purposes. In this paper we describe related work, our research methodology, as well as the current research progress and expected future contributions.

Using Bayesian Network to Estimate the Value of Decisions within the Context of Value-Based Software Engineering: A Multiple Case Study

Context: Companies must make a paradigm shift in which both short- and long-term value aspects are employed to guide their decision-making. Such need is pressing in innovative industries, such as ICT, and is the core of Value-based Software Engineering (VBSE). Objective: This paper details three case studies where value estimation models using Bayesian Network (BN) were built and validated. These estimation models were based upon value-based decisions made by key stakeholders in the contexts of feature selection, test cases execution prioritization, and user interfaces design selection. Methods: All three case studies were carried out according to a Framework called VALUE — improVing decision-mAking reLating to software-intensive prodUcts and sErvices development. This framework includes a mixed-methods approach, comprising several steps to build and validate company-specific value estimation models. Such a building process uses as input data key stakeholders’ decisions (gathered using the Value tool), plus additional input from key stakeholders. Results: Three value estimation BN models were built and validated, and the feedback received from the participating stakeholders was very positive. Conclusions: We detail the building and validation of three value estimation BN models, using a combination of data from past decision-making meetings and also input from key stakeholders.

Toward Methodological Guidelines for Process Theories and Taxonomies in Software Engineering

Software engineering is increasingly concerned with theory because the foundational knowledge comprising theories provides a crucial counterpoint to the practical knowledge expressed through methods and techniques. Fortunately, much guidance is available for generating and evaluating theories for explaining why things happen (variance theories). Unfortunately, little guidance is available concerning theories for explaining how things happen (process theories), or theories for analyzing and understanding situations (taxonomies). This paper therefore attempts to clarify the nature and functions of process theories and taxonomies in software engineering research, and to synthesize methodological guidelines for their generation and evaluation. It further advances the key insight that most process theories are taxonomies with additional propositions, which helps inform their evaluation. The proposed methodological guidance has many benefits: it provides a concise summary of existing guidance from reference disciplines, it adapts techniques from reference disciplines to the software engineering context, and it promotes approaches that better facilitate scientific consensus.

The relationship between personality and decision-making: A Systematic literature review

Abstract Context From a point of view, software development is a set of decisions that need to be made while the software is developed. Many alternatives should be considered, such as the technology to employ, or the most important features to implement. However, many factors can influence one’s decision-making, such as the decision maker’s personality. Objective This paper reports the state of the art with regard to the relationship between decision-makers’ personality and decision-making aspects. Method We conducted a Systematic Literature Review to search and analyze published primary studies that discuss the abovementioned relationship in the context of companies that develop any kind of product or service. Results Despite the recognized influence of personality in decision-making activities, we were not able to find any study in Software Engineering field that discusses this relationship. We included 15 studies and most of them are from Management field, excluding one from Information System field. From these studies, we identified 75 reported relationships between 28 different personality aspects and 30 different decision-making aspects. Conclusion The interest in this topic born on 80’s and it has grown after 2002. However, despite the number of reported relationships, and the number of personalities and decision-making aspects investigated, more research on this topic is necessary. In particular, it is important to verify how someone’s personality influences the decision-making considering the software development context. This can help in improving how a decision is made in software engineering context.

Concrete Syntax with Black Box Parsers

Context: Meta programming consists for a large part of matching, analyzing, and transforming syntax trees. Many meta programming systems process abstract syntax trees, but this requires intimate knowledge of the structure of the data type describing the abstract syntax. As a result, meta programming is error-prone, and meta programs are not resilient to evolution of the structure of such ASTs, requiring invasive, fault-prone change to these programs. Inquiry: Concrete syntax patterns alleviate this problem by allowing the meta programmer to match and create syntax trees using the actual syntax of the object language. Systems supporting concrete syntax patterns, however, require a concrete grammar of the object language in their own formalism. Creating such grammars is a costly and error-prone process, especially for realistic languages such as Java and C++. Approach: In this paper we present Concretely, a technique to extend meta programming systems with pluggable concrete syntax patterns, based on external, black box parsers. We illustrate Concretely in the context of Rascal, an open-source meta programming system and language workbench, and show how to reuse existing parsers for Java, JavaScript, and C++. Furthermore, we propose Tympanic, a DSL to declaratively map external AST structures to Rascal's internal data structures. Tympanic allows implementors of Concretely to solve the impedance mismatch between object-oriented class hierarchies in Java and Rascal's algebraic data types. Both the algebraic data type and AST marshalling code is automatically generated. Knowledge: The conceptual architecture of Concretely and Tympanic supports the reuse of pre-existing, external parsers, and their AST representation in meta programming systems that feature concrete syntax patterns for matching and constructing syntax trees. As such this opens up concrete syntax pattern matching for a host of realistic languages for which writing a grammar from scratch is time consuming and error-prone, but for which industry-strength parsers exist in the wild. Grounding: We evaluate Concretely in terms of source lines of code (SLOC), relative to the size of the AST data type and marshalling code. We show that for real programming languages such as C++ and Java, adding support for concrete syntax patterns takes an effort only in the order of dozens of SLOC. Similarly, we evaluate Tympanic in terms of SLOC, showing an order of magnitude of reduction in SLOC compared to manual implementation of the AST data types and marshalling code. Importance: Meta programming has applications in reverse engineering, reengineering, source code analysis, static analysis, software renovation, domain-specific language engineering, and many others. Processing of syntax trees is central to all of these tasks. Concrete syntax patterns improve the practice of constructing meta programs. The combination of Concretely and Tympanic has the potential to make concrete syntax patterns available with very little effort, thereby improving and promoting the application of meta programming in the general software engineering context.

An exploratory study on the evolution of Android malware quality

AbstractIn the context of software engineering, product software quality measures how well a software artifact is designed and coded. Software products must satisfy nonfunctional properties (eg, reliability, usability, understandability, and maintainability), in order to make maintenance and evolution sustainable in the long period. Software evolution is an issue of interest for the malware writers, too, for 2 reasons. First, to evade detection with the minimum effort, malware writers use to produce “variants,” which are obtained by applying little changes to existing malware. Morevoer, recent studies demonstrated that malware is increasingly improving evasion strategies and infection mechanisms and is using more and more complex payloads. This suggests that malware writers are devoting relevant efforts and skills for producing high‐quality software. For this reason, we wonder whether malware writers are devoting effort to improve the structural quality of their code, too, as it happens in the development of goodware. To investigate this question, we (1) characterize a dataset containing about 20 000 Android applications, divided into goodware and malware ones, relying on the Android API version they require, and (2) compute software quality metrics, divided into 4 categories (ie, dimensional, complexity, object‐oriented, and Android‐oriented metrics) for apps belonging to each population. We then identify evolution trends of these metrics in malware and goodware. The results of our study demonstrate that goodware and malicious applications exhibit similar evolution trends for some of the quality indicators, suggesting that malware writers care about the overall quality of their code. Code quality could be considered an indirect measure of how many and how fast variants of existing malware will be released in the wild.

Semantic Knowledge Management System to Support Software Engineers: Implementation and Static Evaluation through Interviews at Ericsson

Background: In large-scale corporations in the software engineering context information overload problems occur as stakeholders continuously produce useful information on process life-cycle issues, ...