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Metric-centered and technology-independent architectural views for software comprehension

The maintenance of applications is a crucial activity in the software industry. The high cost of this process is due to the effort invested on software comprehension since, in most of cases, there is no up-to-...

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Back to the future: origins and directions of the “Agile Manifesto” – views of the originators

In 2001, seventeen professionals set up the manifesto for agile software development. They wanted to define values and basic principles for better software development. On top of being brought into focus, the ...

Investigating the effectiveness of peer code review in distributed software development based on objective and subjective data

Code review is a potential means of improving software quality. To be effective, it depends on different factors, and many have been investigated in the literature to identify the scenarios in which it adds qu...

On the benefits and challenges of using kanban in software engineering: a structured synthesis study

Kanban is increasingly being used in diverse software organizations. There is extensive research regarding its benefits and challenges in Software Engineering, reported in both primary and secondary studies. H...

Challenges on applying genetic improvement in JavaScript using a high-performance computer

Genetic Improvement is an area of Search Based Software Engineering that aims to apply evolutionary computing operators to the software source code to improve it according to one or more quality metrics. This ...

Actor’s social complexity: a proposal for managing the iStar model

Complex systems are inherent to modern society, in which individuals, organizations, and computational elements relate with each other to achieve a predefined purpose, which transcends individual goals. In thi...

Investigating measures for applying statistical process control in software organizations

The growing interest in improving software processes has led organizations to aim for high maturity, where statistical process control (SPC) is required. SPC makes it possible to analyze process behavior, pred...

An approach for applying Test-Driven Development (TDD) in the development of randomized algorithms

TDD is a technique traditionally applied in applications with deterministic algorithms, in which the input and the expected result are known. However, the application of TDD with randomized algorithms have bee...

Supporting governance of mobile application developers from mining and analyzing technical questions in stack overflow

There is a need to improve the direct communication between large organizations that maintain mobile platforms (e.g. Apple, Google, and Microsoft) and third-party developers to solve technical questions that e...

Working software over comprehensive documentation – Rationales of agile teams for artefacts usage

Agile software development (ASD) promotes working software over comprehensive documentation. Still, recent research has shown agile teams to use quite a number of artefacts. Whereas some artefacts may be adopt...

Development as a journey: factors supporting the adoption and use of software frameworks

From the point of view of the software framework owner, attracting new and supporting existing application developers is crucial for the long-term success of the framework. This mixed-methods study explores th...

Applying user-centered techniques to analyze and design a mobile application

Techniques that help in understanding and designing user needs are increasingly being used in Software Engineering to improve the acceptance of applications. Among these techniques we can cite personas, scenar...

A measurement model to analyze the effect of agile enterprise architecture on geographically distributed agile development

Efficient and effective communication (active communication) among stakeholders is thought to be central to agile development. However, in geographically distributed agile development (GDAD) environments, it c...

A survey of search-based refactoring for software maintenance

This survey reviews published materials related to the specific area of Search-Based Software Engineering that concerns software maintenance and, in particular, refactoring. The survey aims to give a comprehen...

Guest editorial foreword for the special issue on automated software testing: trends and evidence

Similarity testing for role-based access control systems.

Access control systems demand rigorous verification and validation approaches, otherwise, they can end up with security breaches. Finite state machines based testing has been successfully applied to RBAC syste...

An algorithm for combinatorial interaction testing: definitions and rigorous evaluations

Combinatorial Interaction Testing (CIT) approaches have drawn attention of the software testing community to generate sets of smaller, efficient, and effective test cases where they have been successful in det...

How diverse is your team? Investigating gender and nationality diversity in GitHub teams

Building an effective team of developers is a complex task faced by both software companies and open source communities. The problem of forming a “dream”

Investigating factors that affect the human perception on god class detection: an analysis based on a family of four controlled experiments

Evaluation of design problems in object oriented systems, which we call code smells, is mostly a human-based task. Several studies have investigated the impact of code smells in practice. Studies focusing on h...

On the evaluation of code smells and detection tools

Code smells refer to any symptom in the source code of a program that possibly indicates a deeper problem, hindering software maintenance and evolution. Detection of code smells is challenging for developers a...

On the influence of program constructs on bug localization effectiveness

Software projects often reach hundreds or thousands of files. Therefore, manually searching for code elements that should be changed to fix a failure is a difficult task. Static bug localization techniques pro...

DyeVC: an approach for monitoring and visualizing distributed repositories

Software development using distributed version control systems has become more frequent recently. Such systems bring more flexibility, but also greater complexity to manage and monitor multiple existing reposi...

A genetic algorithm based framework for software effort prediction

Several prediction models have been proposed in the literature using different techniques obtaining different results in different contexts. The need for accurate effort predictions for projects is one of the ...

Elaboration of software requirements documents by means of patterns instantiation

Studies show that problems associated with the requirements specifications are widely recognized for affecting software quality and impacting effectiveness of its development process. The reuse of knowledge ob...

ArchReco: a software tool to assist software design based on context aware recommendations of design patterns

This work describes the design, development and evaluation of a software Prototype, named ArchReco, an educational tool that employs two types of Context-aware Recommendations of Design Patterns, to support us...

On multi-language software development, cross-language links and accompanying tools: a survey of professional software developers

Non-trivial software systems are written using multiple (programming) languages, which are connected by cross-language links. The existence of such links may lead to various problems during software developmen...

SoftCoDeR approach: promoting Software Engineering Academia-Industry partnership using CMD, DSR and ESE

The Academia-Industry partnership has been increasingly encouraged in the software development field. The main focus of the initiatives is driven by the collaborative work where the scientific research work me...

Issues on developing interoperable cloud applications: definitions, concepts, approaches, requirements, characteristics and evaluation models

Among research opportunities in software engineering for cloud computing model, interoperability stands out. We found that the dynamic nature of cloud technologies and the battle for market domination make clo...

Game development software engineering process life cycle: a systematic review

Software game is a kind of application that is used not only for entertainment, but also for serious purposes that can be applicable to different domains such as education, business, and health care. Multidisc...

Correlating automatic static analysis and mutation testing: towards incremental strategies

Traditionally, mutation testing is used as test set generation and/or test evaluation criteria once it is considered a good fault model. This paper uses mutation testing for evaluating an automated static anal...

A multi-objective test data generation approach for mutation testing of feature models

Mutation approaches have been recently applied for feature testing of Software Product Lines (SPLs). The idea is to select products, associated to mutation operators that describe possible faults in the Featur...

An extended global software engineering taxonomy

In Global Software Engineering (GSE), the need for a common terminology and knowledge classification has been identified to facilitate the sharing and combination of knowledge by GSE researchers and practition...

A systematic process for obtaining the behavior of context-sensitive systems

Context-sensitive systems use contextual information in order to adapt to the user’s current needs or requirements failure. Therefore, they need to dynamically adapt their behavior. It is of paramount importan...

Distinguishing extended finite state machine configurations using predicate abstraction

Extended Finite State Machines (EFSMs) provide a powerful model for the derivation of functional tests for software systems and protocols. Many EFSM based testing problems, such as mutation testing, fault diag...

Extending statecharts to model system interactions

Statecharts are diagrams comprised of visual elements that can improve the modeling of reactive system behaviors. They extend conventional state diagrams with the notions of hierarchy, concurrency and communic...

On the relationship of code-anomaly agglomerations and architectural problems

Several projects have been discontinued in the history of the software industry due to the presence of software architecture problems. The identification of such problems in source code is often required in re...

An approach based on feature models and quality criteria for adapting component-based systems

Feature modeling has been widely used in domain engineering for the development and configuration of software product lines. A feature model represents the set of possible products or configurations to apply i...

Patch rejection in Firefox: negative reviews, backouts, and issue reopening

Writing patches to fix bugs or implement new features is an important software development task, as it contributes to raise the quality of a software system. Not all patches are accepted in the first attempt, ...

Investigating probabilistic sampling approaches for large-scale surveys in software engineering

Establishing representative samples for Software Engineering surveys is still considered a challenge. Specialized literature often presents limitations on interpreting surveys’ results, mainly due to the use o...

Characterising the state of the practice in software testing through a TMMi-based process

The software testing phase, despite its importance, is usually compromised by the lack of planning and resources in industry. This can risk the quality of the derived products. The identification of mandatory ...

Self-adaptation by coordination-targeted reconfigurations

A software system is self-adaptive when it is able to dynamically and autonomously respond to changes detected either in its internal components or in its deployment environment. This response is expected to ensu...

Templates for textual use cases of software product lines: results from a systematic mapping study and a controlled experiment

Use case templates can be used to describe functional requirements of a Software Product Line. However, to the best of our knowledge, no efforts have been made to collect and summarize these existing templates...

F3T: a tool to support the F3 approach on the development and reuse of frameworks

Frameworks are used to enhance the quality of applications and the productivity of the development process, since applications may be designed and implemented by reusing framework classes. However, frameworks ...

NextBug: a Bugzilla extension for recommending similar bugs

Due to the characteristics of the maintenance process followed in open source systems, developers are usually overwhelmed with a great amount of bugs. For instance, in 2012, approximately 7,600 bugs/month were...

Assessing the benefits of search-based approaches when designing self-adaptive systems: a controlled experiment

The well-orchestrated use of distilled experience, domain-specific knowledge, and well-informed trade-off decisions is imperative if we are to design effective architectures for complex software-intensive syst...

Revealing influence of model structure and test case profile on the prioritization of test cases in the context of model-based testing

Test case prioritization techniques aim at defining an order of test cases that favor the achievement of a goal during test execution, such as revealing failures as earlier as possible. A number of techniques ...

A metrics suite for JUnit test code: a multiple case study on open source software

The code of JUnit test cases is commonly used to characterize software testing effort. Different metrics have been proposed in literature to measure various perspectives of the size of JUnit test cases. Unfort...

Designing fault-tolerant SOA based on design diversity

Over recent years, software developers have been evaluating the benefits of both Service-Oriented Architecture (SOA) and software fault tolerance techniques based on design diversity. This is achieved by creat...

Method-level code clone detection through LWH (Light Weight Hybrid) approach

Many researchers have investigated different techniques to automatically detect duplicate code in programs exceeding thousand lines of code. These techniques have limitations in finding either the structural o...

The problem of conceptualization in god class detection: agreement, strategies and decision drivers

The concept of code smells is widespread in Software Engineering. Despite the empirical studies addressing the topic, the set of context-dependent issues that impacts the human perception of what is a code sme...

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Development of Software Engineering: A Research Perspective

• Published: September 2006
• Volume 21 , pages 682–696, ( 2006 )

Cite this article

• Hong Mei 1 ,
• Dong-Gang Cao 1 &
• Fu-Qing Yang 1  

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In the past 40 years, software engineering has emerged as an important sub-field of computer science and has made significant contribution to the software industry. Now it is gradually becoming a new independent discipline. This paper presents a survey of software engineering development from a research perspective. Firstly, the history of software engineering is reviewed with focus on the driving forces of software technology, the software engineering framework and the milestones of software engineering development. Secondly, after reviewing the past academic efforts, the current research activities are surveyed and new challenges brought by Internet are analyzed. Software engineering researches and activities in China are also reviewed. The work in Peking University is described as a representative.

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Hong Mei, Dong-Gang Cao & Fu-Qing Yang

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Survey: This work was supported by several government-funded projects, including the National Key Projects in the State 6th to 10th Five-Year Plan, the National High Technology Development 863 Program of China, the National Grand Fundamental Research 973 Program of China, and the National Natural Science Foundation of China, etc.

Hong Mei is a professor in computer science at the Peking University, China. He got the B.Eng. and M.S. degrees in computer science from Nanjing University of Aeronautics & Astronautics (NUAA) in 1984 and 1987 respectively, and the Ph.D. degree in computer science from Shanghai Jiao Tong University in 1992. His current research interests include: software engineering and software engineering environment, software reuse and software component technology, distributed object technology, etc.

Dong-Gang Cao is an assistant professor in the School of Electronics Engineering and Computer Science at the Peking University, China. He received the B.Eng. and M.Eng. degrees in computer science & technology from the Beijing University of Posts and Telecommunications in 1998 and 2001 respectively, and the Ph.D. degree in computer science from Peking University in 2005. His research interests include software engineering, Internet technologies and component-based software development.

Fu-Qing Yang is a professor in computer science in the School of Electronics Engineering and Computer Science, the Dean of the Faculty of Information and Engineering Sciences, Peking University. She is an academician of the Chinese Academy of Sciences and an IEEE fellow.

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Mei, H., Cao, DG. & Yang, FQ. Development of Software Engineering: A Research Perspective. J Comput Sci Technol 21 , 682–696 (2006). https://doi.org/10.1007/s11390-006-0682-8

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Received : 17 April 2006

Revised : 27 June 2006

Issue Date : September 2006

DOI : https://doi.org/10.1007/s11390-006-0682-8

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During Fiscal Year 2023 (FY 2023) – from October 1, 2022, through September 30, 2023 –the NIST Information Technology Laboratory (ITL) Cybersecurity and Privacy Program successfully responded to numerous challenges and opportunities in security and privacy. This Annual Report highlights the FY 2023 research activities for the ITL Cybersecurity and Privacy Program, including the ongoing participation and development of international standards; research and practical applications in several key priority areas (e.g., Post Quantum Cryptography, updating the NIST Cybersecurity Framework (CSF 2.0) and some new CSF profiles); accomplishments in the area of improving software and supply chain cybersecurity; IoT cybersecurity guidelines work; National Cybersecurity Center of Excellence (NCCoE) projects, and setting up a new comment site for NIST’s Risk Management Framework work; release of a Phish scale; progress in the Identity and Access Management program; Strategic and Emerging Research Initiatives (SERI) for autonomous vehicles.

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Publication: https://doi.org/10.6028/NIST.SP.800-229 Download URL

Supplemental Material: None available

Related NIST Publications: SP 800-225

Document History: 05/20/24: SP 800-229 (Final)

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A Modular End-to-End Framework for Secure Firmware Updates on Embedded Systems

Firmware refers to device read-only resident code which includes microcode and macro-instruction-level routines. For Internet-of-Things (IoT) devices without an operating system, firmware includes all the necessary instructions on how such embedded systems operate and communicate. Thus, firmware updates are essential parts of device functionality. They provide the ability to patch vulnerabilities, address operational issues, and improve device reliability and performance during the lifetime of the system. This process, however, is often exploited by attackers in order to inject malicious firmware code into the embedded device. In this article, we present a framework for secure firmware updates on embedded systems. This approach is based on hardware primitives and cryptographic modules, and it can be deployed in environments where communication channels might be insecure. The implementation of the framework is flexible, as it can be adapted in regards to the IoT device’s available hardware resources and constraints. Our security analysis shows that our framework is resilient to a variety of attack vectors. The experimental setup demonstrates the feasibility of the approach. By implementing a variety of test cases on FPGA, we demonstrate the adaptability and performance of the framework. Experiments indicate that the update procedure for a 1183-kB firmware image could be achieved, in a secure manner, under 1.73 seconds.

Computer development based embedded systems in precision agriculture: tools and application

Low-power on-chip implementation of enhanced svm algorithm for sensors fusion-based activity classification in lightweighted edge devices.

Smart homes assist users by providing convenient services from activity classification with the help of machine learning (ML) technology. However, most of the conventional high-performance ML algorithms require relatively high power consumption and memory usage due to their complex structure. Moreover, previous studies on lightweight ML/DL models for human activity classification still require relatively high resources for extremely resource-limited embedded systems; thus, they are inapplicable for smart homes’ embedded system environments. Therefore, in this study, we propose a low-power, memory-efficient, high-speed ML algorithm for smart home activity data classification suitable for an extremely resource-constrained environment. We propose a method for comprehending smart home activity data as image data, hence using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of three parts: data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding from each cluster of preprocessed data. Finally, the classification process classifies input data by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. We verified our algorithm on `Raspberry Pi 3’ and `STM32 Discovery board’ embedded systems by loading trained hyperplanes and performing classification on 1000 training data. Compared to a linear support vector machine implemented from Tensorflow Lite, the proposed algorithm improved memory usage to 15.41%, power consumption to 41.7%, performance up to 50.4%, and power per accuracy to 39.2%. Moreover, compared to a convolutional neural network model, the proposed model improved memory usage to 15.41%, power consumption to 61.17%, performance to 57.6%, and power per accuracy to 55.4%.

Getting Started with Secure Embedded Systems

Design principles for embedded systems, cyense: cyclic energy-aware scheduling for energy-harvested embedded systems, embedded systems software development, esqumo an embedded software quality model.

Embedded systems are increasingly used in our daily life due to their importance. They are computer platforms consisting of hardware and software. They run specific tasks to realize functional and non functional requirements. Several specific quality attributes were identified as relevant to the embedded system domain. However, the existent general quality models do not address clearly these specific quality attributes. Hence, the proposition of quality models which address the relevant quality attributes of embedded systems needs more attention and investigation. The major goal of this paper is to propose a new quality model (called ESQuMo for Embedded Software Quality Model) which provides a better understanding of quality in the context of embedded software. Besides, it focuses the light on the relevant attributes of the embedded software and addresses clearly the importance of these attributes. In fact, ESQuMo is based on the well-established ISO/IEC 25010 standard quality model.

Embedded Systems and Architectures

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Stanford-led study links school environment to brain development.

For decades, researchers have linked differences in school-age children’s brain development to their out-of-school environment, using indirect socioeconomic factors such as parental income and neighborhood characteristics. 

In a new paper , researchers from Stanford Graduate School of Education (GSE) demonstrate for the first time that, even when controlling for those other factors, there is a direct link between a child’s school environment and the development of their white matter, or the network of nerve fibers that allows different parts of the brain to communicate. 

In other words, schools that do better than average at promoting learning are showing greater year-by-year advances in brain development, even for students coming from a wide range of socioeconomic environments. 

For their study, the authors, including GSE doctoral candidate Ethan Roy , Professor Bruce McCandliss , and Associate Professor Jason Yeatman , leveraged data from the Adolescent Brain Cognitive Development (ABCD) Study, the largest long-term study of brain development and child health in the United States, and the Stanford Education Data Archive (SEDA), a national database of academic performance developed by the Educational Opportunity Project at Stanford University. 

Their findings show that children who attend higher-performing schools have accelerated white matter development, including in an area of the brain closely associated with reading skills.

Roy said the results, published in Developmental Cognitive Neuroscience on April 26, were “striking.”

“What jumped off the page for us is that, even when controlling for things like parental income, parental education, neighborhood context, and household conflict levels, we were still able to observe a significant relationship between the school environment of an individual and growth properties of their brain,” he said.

Filling a gap in learning science research

Yeatman, who along with McCandliss serves as an advisor to Roy, said the study is the first to show how variation in the educational opportunities afforded to children is related to brain development.

“Essentially, two children from similar families who are born on two sides of a school boundary have measurable differences in how their brains wire together,” said Yeatman, who holds a joint faculty appointment at the GSE and Stanford Medicine, is a faculty affiliate of the Stanford Accelerator for Learning , and directs the Brain Development & Education Lab and Rapid Online Assessment of Reading . 

The study looked at fractional anisotropy, a measure of how water moves through brain tissue and an indication of how insulated, or myelinated, a neuron’s axons are (higher myelination increases the speed of transmission between neurons and is associated with improved learning). The observational results show that fractional anisotropy is directly linked to a school’s national grade equivalence score, or a measure of how third graders from that school perform compared with the national average.

The paper fills a gap in learning science research. Although past studies have linked socioeconomic status to white matter development, they have not been able to focus in on specific attributes of a child’s development, such as the school they attend. Other research — including from Yeatman’s lab — has shown that educational interventions can lead to changes in white matter, but those have been relatively small-scale studies with participants who are not representative of the broader population. 

“This is one of the first cases where we can measure the thing we actually care about at the population level,” Yeatman said.

The authors also trained a deep learning model to conduct a global analysis of white matter, finding that children who attend schools with higher SEDA scores had brains that appeared developmentally “more mature” than their chronological age.

A measurable impact

The implications are “potentially game-changing,” said McCandliss, who directs the Stanford Educational Neuroscience Initiative (SENSI) and is a faculty affiliate of the Stanford Accelerator for Learning. 

“National discussions of the importance of elementary school quality have never before been framed in terms of having a measurable impact on physical brain development of our young children,” he said. “I think this changes the frame of the discussion and decision-making around the impact of inequity.”

The study was only possible because of the comprehensive data included in the ABCD Study and SEDA, the researchers said. McCandliss, an investigator in the ABCD Study, first approached the ABCD team leaders about linking the SEDA data with the ABCD data in 2018, and his SENSI team spent about two years creating the resulting “crosswalk.” 

McCandliss called the ABCD study a “dream come true,” and the linked data a way to “finally” answer “elusive questions about how inequities in educational opportunities may actually be changing the course of physical and functional brain development during the vulnerable elementary school years across the nation.”

To analyze the brain white matter from the MRI data included in the ABCD study, the authors used pyAFQ , an open-source software developed by Yeatman’s lab. “It was a really fruitful collaboration across both labs,” Roy said.

The authors hope their methods and the newly linked ABCD and SEDA data, which is now freely available to a community of registered researchers around the world, will allow other scholars to pursue their own ideas and hypotheses at the intersection of education and neuroscience.

Yeatman said the methods and data used in the study will allow researchers to be more precise about environmental factors linked to brain development and the mechanisms behind those connections.

“The environment influences brain development,” he said. “That’s obvious. But what about the environment influences brain development? This is the first step in actually unraveling that specificity.”

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