Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userHappiness and the productivity of software engineers
119
0
0.0
(
0
)
Added by
Daniel Graziotin
Publication date
2019
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Software companies and startups often follow the idea of flourishing happiness among developers. Perks, playground rooms, free breakfast, remote office options, sports facilities near the companies, company retreats, you name it. The rationale is that happy developers should be more productive and also retained. But is it the case that happy software engineers are more productive? Moreover, are perks the way to go to make developers happy? Are developers happy at all? What are the consequences of unhappiness among software engineers? These questions are important to ask both from the perspective of productivity and from the perspective of sustainable software development and well-being in the workplace. Managers, team leaders, as well as team members should be interested in these concerns. This chapter provides an overview of our studies on the happiness of software developers. You will learn why it is important to make software developers happy, how happy they really are, what makes them unhappy, and what is expected regarding happiness and productivity while developing software.
rate research
Read More
Following the onset of the COVID-19 pandemic and subsequent lockdowns, software engineers daily life was disrupted and abruptly forced into remote working from home. This change deeply impacted typical working routines, affecting both well-being and productivity. Moreover, this pandemic will have long-lasting effects in the software industry, with several tech companies allowing their employees to work from home indefinitely if they wish to do so. Therefore, it is crucial to analyze and understand how a typical working day looks like when working from home and how individual activities affect software developers well-being and productivity. We performed a two-wave longitudinal study involving almost 200 globally carefully selected software professionals, inferring daily activities with perceived well-being, productivity, and other relevant psychological and social variables. Results suggest that the time software engineers spent doing specific activities from home was similar when working in the office. However, we also found some significant mean differences. The amount of time developers spent on each activity was unrelated to their well-being, perceived productivity, and other variables. We conclude that working remotely is not per se a challenge for organizations or developers.
Software engineers working in large projects must navigate complex information landscapes. Change Impact Analysis (CIA) is a task that relies on engineers successful information seeking in databases storing, e.g., source code, requirements, design descriptions, and test case specifications. Several previous approaches to support information seeking are task-specific, thus understanding engineers seeking behavior in specific tasks is fundamental. We present an industrial case study on how engineers seek information in CIA, with a particular focus on traceability and development artifacts that are not source code. We show that engineers have different information seeking behavior, and that some do not consider traceability particularly useful when conducting CIA. Furthermore, we observe a tendency for engineers to prefer less rigid types of support rather than formal approaches, i.e., engineers value support that allows flexibility in how to practically conduct CIA. Finally, due to diverse information seeking behavior, we argue that future CIA support should embrace individual preferences to identify change impact by empowering several seeking alternatives, including searching, browsing, and tracing.
Researchers are increasingly recognizing the importance of human aspects in software development and since qualitative methods are used to, in-depth, explore human behavior, we believe that studies using such techniques will become more common. Existing qualitative software engineering guidelines do not cover the full breadth of qualitative methods and knowledge on using them found in the social sciences. The aim of this study was thus to extend the software engineering research communitys current body of knowledge regarding available qualitative methods and provide recommendations and guidelines for their use. With the support of an epistemological argument and a literature review, 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.
The purpose of this study is to introduce software technologies and models and artificial intelligence algorithms to improve the weaknesses of CBT (Cognitive Behavior Therapy) method in psychotherapy. The presentation method for this purpose is the implementation of psychometric experiments in which the hidden human variables are inferred from the answers of tests. In this report, we describe the various models of Item Response Theory and measure the hidden components of ability and complementary parameters of the reality of the individuals situation. Psychometrics, selecting the appropriate model and estimating its parameters have been introduced and implemented using R language developed libraries. Due to the high flexibility of the Multi variant Rasch mixture Model, machine learning has been applied to this method of data modeling. BIC and CML were used to determine the number of hidden classes of the model and its parameters respectively, to obtain Measurement Invariance. The sensitivity of items to hidden attributes varies between groups (DIF), so methods for detecting it are introduced. This simulation is done based on the Verbal Aggression Dataset. We also analyze and compile a reference model based on this certificate based on the discovered patterns of software engineering. Other achievements of this study are related to providing a solution to explain the reengineering problems of the mind, by preparing an identity card for the clients by an ontology. Finally, applying the developed knowledge in the form of system thinking and recommended patterns in software engineering during the treatment process is pointed out.
Many science advances have been possible thanks to the use of research software, which has become essential to advancing virtually every Science, Technology, Engineering and Mathematics (STEM) discipline and many non-STEM disciplines including social sciences and humanities. And while much of it is made available under open source licenses, work is needed to develop, support, and sustain it, as underlying systems and software as well as user needs evolve. In addition, the changing landscape of high-performance computing (HPC) platforms, where performance and scaling advances are ever more reliant on software and algorithm improvements as we hit hardware scaling barriers, is causing renewed tension between sustainability of software and its performance. We must do more to highlight the trade-off between performance and sustainability, and to emphasize the need for sustainability given the fact that complex software stacks dont survive without frequent maintenance; made more difficult as a generation of developers of established and heavily-used research software retire. Several HPC forums are doing this, and it has become an active area of funding as well. In response, the authors organized and ran a panel at the SC18 conference. The objectives of the panel were to highlight the importance of sustainability, to illuminate the tension between pure performance and sustainability, and to steer SC community discussion toward understanding and addressing this issue and this tension. The outcome of the discussions, as presented in this paper, can inform choices of advance compute and data infrastructures to positively impact future research software and future research.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
