Experiences from Applying the Karlskrona Manifesto Principles for Sustainability in Software System Design

Sustainability in software design is an evolving area that requires more practical guide on how software designers, developers and requirement engineers can elicit software sustain- ability requirements. The Karlskrona Manifesto for Sustainabil- ity Design (KMSD) principles serve as a common ground to guide and support sustainability in software design.However, there is little research as of now showing how these KMSD principles are applied in software requirements elicitation and software design in general. This paper presents some of our evaluation of how these KMSD principles, the software sustaina- bility requirement template and software sustainability require- ment best practice template were applied in two case studies by stakeholders (requirement engineers, CTO and software develop- ers)

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

Measuring usability for application software using the quality in use integration measurement model

User interfaces of application software are designed to make user interaction as efficient and as simple as possible. Market accessibility of any application software is determined by the usability of its user interfaces. A poorly designed user interface will have little value no matter how powerful the program is. Thus, it is significantly important to measure usability during the system development lifecycle in order to avoid user disappointment. Various methods and standards that help measure usability have been developed. However, these methods define usability inconsistently, which makes software engineers hesitant in implementing these methods or standards. The Quality in Use Integrated Measurement (QUIM) model is a consolidated approach for measuring usability through 10 factors, 26 criteria, and 127 metrics. It decomposes usability into factors, criteria, and metrics, and it is a hierarchical model that helps developers with no or little background of usability metrics. Among 127 metrics of QUIM, essential efficiency (EE) is the most specific metric used to measure the usability of user interfaces through an equation. This study involves a comparative analysis between three case studies that use the QUIM model to measure usability in terms of EE for three case studies: (1) Public University Registration System, (2) Restaurant Menu Ordering System, and (3) ATM system. A comparison is made based on the percentage of EE for each element of the use cases in each use case diagram. The results obtained revealed that the user interface design for Restaurant Menu Ordering System scored the highest percentage of EE, thus proving to be the most user-friendly application software among its counterparts

Giving RSEs a Larger Stage through the Better Scientific Software Fellowship

The Better Scientific Software Fellowship (BSSwF) was launched in 2018 to foster and promote practices, processes, and tools to improve developer productivity and software sustainability of scientific codes. BSSwF's vision is to grow the community with practitioners, leaders, mentors, and consultants to increase the visibility of scientific software production and sustainability. Over the last five years, many fellowship recipients and honorable mentions have identified as research software engineers (RSEs). This paper provides case studies from several of the program's participants to illustrate some of the diverse ways BSSwF has benefited both the RSE and scientific communities. In an environment where the contributions of RSEs are too often undervalued, we believe that programs such as BSSwF can be a valuable means to recognize and encourage community members to step outside of their regular commitments and expand on their work, collaborations and ideas for a larger audience.Comment: submitted to Computing in Science & Engineering (CiSE), Special Issue on the Future of Research Software Engineers in the U

A Process-oriented Approach for Migrating Software to Heterogeneous Platforms

Context: Heterogeneous computing, i.e., computing performed on processors of different types - such as combination of CPUs and GPUs, or CPUs and FPGAs - has shown to be a feasible path towards higher performance and less energy consumption. However, this approach imposes a number of challenges on the software side that must be addressed in order to achieve the aforementioned advantages.Objective: The objective of this thesis is to improve the process of software deployment on heterogeneous platforms. Through a detailed analysis of the state-of-the-art and state-of-the-practice, we aim to provide a reasoning framework for engineers to migrate software to be executed on such platforms.Method: To achieve our goal, we conducted: (i) a literature review in the form of a systematic mapping study on software deployment on heterogeneous platforms; (ii) a multiple case study in industry that highlights the main challenges and concerns in the state-of-the-practice in the area; and (iii) a study in which we propose and evaluate a decision framework to guide engineers in migrating software for execution on heterogeneous platforms, with a case study in the automotive domain.Results: In the mapping study, we provided a thorough classification of the identified concerns and approaches to deploying software on heterogeneous platforms. Among other findings, we discovered a lack of holistic approaches that include development processes, as well as few validation studies in industrial contexts. In the second study, we discovered and analyzed common practices and challenges that companies face when using heterogeneous platforms. One of such challenges is related to the lack of approaches that cover the software development lifecycle. In the third study, we proposed a decision framework that guides engineers in the process of reasoning for migrating software for execution on heterogeneous platforms. It consists of five stages (assessing, re-architecting, developing, deploying, evaluating), each containing a set of aspects to be addressed through the answers to predefined questions.Conclusions: This thesis addresses a gap that was identified in both theory and practice concerning the lack of holistic approaches to migrate software for execution on heterogeneous platforms. Our proposed approach addresses the problem through systematic guidance for engineers.Future work: In the future, we intend to further refine the proposed framework through case studies in domains other than automotive. We will explore its integration with existing software engineering processes in industrial contexts, performing in-depth analysis of the required adaptations and providing detailed solutions within the stages of the framework

Understanding How Reverse Engineers Make Sense of Programs from Assembly Language Representations

This dissertation develops a theory of the conceptual and procedural aspects involved with how reverse engineers make sense of executable programs. Software reverse engineering is a complex set of tasks which require a person to understand the structure and functionality of a program from its assembly language representation, typically without having access to the program\u27s source code. This dissertation describes the reverse engineering process as a type of sensemaking, in which a person combines reasoning and information foraging behaviors to develop a mental model of the program. The structure of knowledge elements used in making sense of executable programs are elicited from a case study, interviews with subject matter experts, and observational studies with software reverse engineers. The results from this research can be used to improve reverse engineering tools, to develop training requirements for reverse engineers, and to develop robust computational models of human comprehension in complex tasks where sensemaking is required

Towards a Reference Architecture with Modular Design for Large-scale Genotyping and Phenotyping Data Analysis: A Case Study with Image Data

With the rapid advancement of computing technologies, various scientific research communities have been extensively using cloud-based software tools or applications. Cloud-based applications allow users to access software applications from web browsers while relieving them from the installation of any software applications in their desktop environment. For example, Galaxy, GenAP, and iPlant Colaborative are popular cloud-based systems for scientific workflow analysis in the domain of plant Genotyping and Phenotyping. These systems are being used for conducting research, devising new techniques, and sharing the computer assisted analysis results among collaborators. Researchers need to integrate their new workflows/pipelines, tools or techniques with the base system over time. Moreover, large scale data need to be processed within the time-line for more effective analysis. Recently, Big Data technologies are emerging for facilitating large scale data processing with commodity hardware. Among the above-mentioned systems, GenAp is utilizing the Big Data technologies for specific cases only. The structure of such a cloud-based system is highly variable and complex in nature. Software architects and developers need to consider totally different properties and challenges during the development and maintenance phases compared to the traditional business/service oriented systems. Recent studies report that software engineers and data engineers confront challenges to develop analytic tools for supporting large scale and heterogeneous data analysis. Unfortunately, less focus has been given by the software researchers to devise a well-defined methodology and frameworks for flexible design of a cloud system for the Genotyping and Phenotyping domain. To that end, more effective design methodologies and frameworks are an urgent need for cloud based Genotyping and Phenotyping analysis system development that also supports large scale data processing. In our thesis, we conduct a few studies in order to devise a stable reference architecture and modularity model for the software developers and data engineers in the domain of Genotyping and Phenotyping. In the first study, we analyze the architectural changes of existing candidate systems to find out the stability issues. Then, we extract architectural patterns of the candidate systems and propose a conceptual reference architectural model. Finally, we present a case study on the modularity of computation-intensive tasks as an extension of the data-centric development. We show that the data-centric modularity model is at the core of the flexible development of a Genotyping and Phenotyping analysis system. Our proposed model and case study with thousands of images provide a useful knowledge-base for software researchers, developers, and data engineers for cloud based Genotyping and Phenotyping analysis system development