ICSEA 2022: the seventeenth international conference on software engineering advances

The Seventeenth International Conference on Software Engineering Advances (ICSEA 2022), held between October 16th and October 20th, 2022, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. Several tracks were proposed to treat the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learned. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. Other advanced aspects are related to on-time practical aspects, such as run-time vulnerability checking, rejuvenation process, updates partial or temporary feature deprecation, software deployment and configuration, and on-line software updates. These aspects trigger implications related to patenting, licensing, engineering education, new ways for software adoption and improvement, and ultimately, to software knowledge management. There are many advanced applications requiring robust, safe, and secure software: disaster recovery applications, vehicular systems, biomedical-related software, biometrics related software, mission critical software, E-health related software, crisis-situation software. These applications require appropriate software engineering techniques, metrics and formalisms, such as, software reuse, appropriate software quality metrics, composition and integration, consistency checking, model checking, provers and reasoning. The nature of research in software varies slightly with the specific discipline researchers work in, yet there is much common ground and room for a sharing of best practice, frameworks, tools, languages and methodologies. Despite the number of experts we have available, little work is done at the meta level, that is examining how we go about our research, and how this process can be improved. There are questions related to the choice of programming language, IDEs and documentation styles and standard. Reuse can be of great benefit to research projects yet reuse of prior research projects introduces special problems that need to be mitigated. The research environment is a mix of creativity and systematic approach which leads to a creative tension that needs to be managed or at least monitored. Much of the coding in any university is undertaken by research students or young researchers. Issues of skills training, development and quality control can have significant effects on an entire department. In an industrial research setting, the environment is not quite that of industry as a whole, nor does it follow the pattern set by the university. The unique approaches and issues of industrial research may hold lessons for researchers in other domains. We take here the opportunity to warmly thank all the members of the ICSEA 2022 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to ICSEA 2022. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the ICSEA 2022 organizing committee for their help in handling the logistics of this event. We hope that ICSEA 2022 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in software engineering advances

Metaverso está surgindo!

Human beings being curious and experimental in nature always look forward to making good use of any technology and latest is Metaverse which is a combination of Interactive tools and Technologies like Digital Games, Augmented and Virtual Reality, social media, and Internet. This paper, which is based on literature review explains the Metaverse, its uses and challenges, discusses an example – PartyNite, and future scope of Metaverse.Los seres humanos, curiosos y experimentales por naturaleza, siempre buscan hacer un buen uso de cualquier tecnología y la última es Metaverse, que es una combinación de herramientas y tecnologías interactivas como juegos digitales, realidad aumentada y virtual, redes sociales e Internet. Este artículo, basado en una revisión de la literatura, explica el Metaverso, sus usos y desafíos, analiza un ejemplo: PartyNite y el alcance futuro del Metaverso.Os seres humanos, curiosos e experimentais por natureza, sempre procuram fazer bom uso de qualquer tecnologia e o mais recente é o Metaverse, que é uma combinação de ferramentas e tecnologias interativas, como jogos digitais, realidade aumentada e virtual, mídia social e Internet. Este artigo, baseado na revisão da literatura, explica o Metaverso, seus usos e desafios, discute um exemplo – PartyNite, e o escopo futuro do Metaverso

Prioritizing Tasks in Code Repair: A Psychological Exploration of Computer Code

The current study explored the influence of task prioritization on how computer programmers reviewed and edited code. Forty-five programmers recruited from Amazon Mechanical Turk downloaded and edited a computer program in C#. Programmers were given instructions to review the code and told to prioritize either the reputation, transparency, or performance aspects of the code, or were given no prioritization instruction. Code changes and remarks about their changes to the code were analyzed with a between-within multivariate analysis of variance. Results indicate prioritizing an aspect of the code leads to increased performance on that aspect, but with deficits to other aspects of the code. Managers may want programmers to prioritize certain aspects of code depending on the stage of development of the software (i.e., testing, rollout, etc.). However, managers should also be cognizant of the effects task prioritization has on programmer perceptions of the code as a whole

Preemptive type checking in dynamically typed programs

With the rise of languages such as JavaScript, dynamically typed languages have gained a strong foothold in the programming language landscape. These languages are very well suited for rapid prototyping and for use with agile programming methodologies. However, programmers would benefit from the ability to detect type errors in their code early, without imposing unnecessary restrictions on their programs.Here we describe a new type inference system that identifies potential type errors through a flow-sensitive static analysis. This analysis is invoked at a very late stage, after the compilation to bytecode and initialisation of the program. It computes for every expression the variable’s present (from the values that it has last been assigned) and future (with which it is used in the further program execution) types, respectively. Using this information, our mechanism inserts type checks at strategic points in the original program. We prove that these checks, inserted as early as possible, preempt type errors earlier than existing type systems. We further show that these checks do not change the semantics of programs that do not raise type errors.Preemptive type checking can be added to existing languages without the need to modify the existing runtime environment. We show this with an implementation for the Python language and demonstrate its effectiveness on a number of benchmarks

AUTOMATING DATA-LAYOUT DECISIONS IN DOMAIN-SPECIFIC LANGUAGES

A long-standing challenge in High-Performance Computing (HPC) is the simultaneous achievement of programmer productivity and hardware computational efficiency. The challenge has been exacerbated by the onset of multi- and many-core CPUs and accelerators. Only a few expert programmers have been able to hand-code domain-specific data transformations and vectorization schemes needed to extract the best possible performance on such architectures. In this research, we examined the possibility of automating these methods by developing a Domain-Specific Language (DSL) framework. Our DSL approach extends C++14 by embedding into it a high-level data-parallel array language, and by using a domain-specific compiler to compile to hybrid-parallel code. We also implemented an array index-space transformation algebra within this high-level array language to manipulate array data-layouts and data-distributions. The compiler introduces a novel method for SIMD auto-vectorization based on array data-layouts. Our new auto-vectorization technique is shown to outperform the default auto-vectorization strategy by up to 40% for stencil computations. The compiler also automates distributed data movement with overlapping of local compute with remote data movement using polyhedral integer set analysis. Along with these main innovations, we developed a new technique using C++ template metaprogramming for developing embedded DSLs using C++. We also proposed a domain-specific compiler intermediate representation that simplifies data flow analysis of abstract DSL constructs. We evaluated our framework by constructing a DSL for the HPC grand-challenge domain of lattice quantum chromodynamics. Our DSL yielded performance gains of up to twice the flop rate over existing production C code for selected kernels. This gain in performance was obtained while using less than one-tenth the lines of code. The performance of this DSL was also competitive with the best hand-optimized and hand-vectorized code, and is an order of magnitude better than existing production DSLs.Doctor of Philosoph

Activity Report 2012. Project-Team RMOD. Analyses and Languages Constructs for Object-Oriented Application Evolution

Activity Report 2012 Project-Team RMOD Analyses and Languages Constructs for Object-Oriented Application Evolutio

Performance engineering of data-intensive applications

Data-intensive programs deal with big chunks of data and often contain compute-intensive characteristics. Among various HPC application domains, big data analytics, machine learning and the more recent deep-learning models are well-known data-intensive applications. An efficient design of such applications demands extensive knowledge of the target hardware and software, particularly the memory/cache hierarchy and the data communication among threads/processes. Such a requirement makes code development an arduous task, as inappropriate data structures and algorithm design may result in superfluous runtime, let alone hardware incompatibilities while porting the code to other platforms. In this dissertation, we introduce a set of tools and methods for the performance engineering of parallel data-intensive programs. We start with performance profiling to gain insights on thread communications and relevant code optimizations. Then, by narrowing down our scope to deep-learning applications, we introduce our tools for enhancing the performance portability and scalability of convolutional neural networks (ConvNet) at inference and training phases. Our first contribution is a novel performance-profiling method to unveil potential communication bottlenecks caused by data-access patterns and thread interactions. Our findings show that the data shared between a pair of threads should be reused with a reasonably short intervals to preserve data locality, yet existing profilers neglect them and mainly report the communication volume. We propose new hardware-independent metrics to characterize thread communication and provide suggestions for applying appropriate optimizations on a specific code region. Our experiments show that applying relevant optimizations improves the performance in Rodinia benchmarks by up to 56%. For the next contribution, we developed a framework for automatic generation of efficient and performance-portable convolution kernels, including Winograd convolutions, for various GPU platforms. We employed a synergy of meta-programming, symbolic execution, and auto-tuning. The results demonstrate efficient kernels generated through an automated optimization pipeline with runtimes close to vendor deep-learning libraries, and the minimum required programming effort confirms the performance portability of our approach. Furthermore, our symbolic execution method exploits repetitive patterns in Winograd convolutions, enabling us to reduce the number of arithmetic operations by up to 62% without compromising the numerical stability. Lastly, we investigate possible methods to scale the performance of ConvNets in training and inference phases. Our specialized training platform equipped with a novel topology-aware network pruning algorithm enables rapid training, neural architecture search, and network compression. Thus, an AI model training can be easily scaled to a multitude of compute nodes, leading to faster model design with less operating costs. Furthermore, the network compression component scales a ConvNet model down by removing redundant layers, preparing the model for a more pertinent deployment. Altogether, this work demonstrates the necessity and shows the benefit of performance engineering and parallel programming methods in accelerating emerging data-intensive workloads. With the help of the proposed tools and techniques, we pinpoint data communication bottlenecks and achieve performance portability and scalability in data-intensive applications