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

SOFTENG 2023: the ninth international conference on advances and trends in software engineering

The Ninth International Conference on Advances and Trends in Software Engineering (SOFTENG 2023), held between April 24th and April 28th, 2023, continued a series of events focusing on these challenging aspects for software development and deployment, across the whole life-cycle. Software engineering exhibits challenging dimensions in the light of new applications, devices, and services. Mobility, user-centric development, smart-devices, e-services, ambient environments, e-health and wearable/implantable devices pose specific challenges for specifying software requirements and developing reliable and safe software. Specific software interfaces, agile organization and software dependability require particular approaches for software security, maintainability, and sustainability. We take here the opportunity to warmly thank all the members of the SOFTENG 2023 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 SOFTENG 2023. 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 SOFTENG 2023 organizing committee for their help in handling the logistics of this event. We hope that SOFTENG 2023 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in the field of software engineering

An Extended Study of the Correlation of Cognitive Complexity-related Code Measures

Several measures have been proposed to represent various characteristics of code, such as size, complexity, cohesion, coupling, etc. These measures are deemed interesting because the internal characteristics they measure (which are not interesting per se) are believed to be correlated with external software qualities (like reliability, maintainability, etc.) that are definitely interesting for developers or users. Although many measures have been proposed for software code, new measures are continuously proposed. However, before starting using a new measure, we would like to ascertain that it is actually useful and that it provides some improvement with respect to well established measures that have been in use for a long time and whose merits have been widely evaluated. In 2018, a new code measure, named “Cognitive Complexity” was proposed. According to the proposers, this measure should correlate to code understandability much better than traditional code measures, such as McCabe Complexity, for instance. However, hardly any experimentation proved whether the “Cognitive Complexity” measure is better than other measures or not. Actually, it was not even verified whether the new measure provides different knowledge concerning code with respect to traditional measures. In this paper, we aim at evaluating experimentally to what extent the new measure is correlated with traditional measures. To this end, we measured the code from a set of open-source Java projects and derived models of “Cognitive Complexity” based on the traditional code measures yielded by a state-of-the-art code measurement tool. We found that fairly accurate models of “Cognitive Complexity” can be obtained using just a few traditional code measures. In this sense, the “Cognitive Complexity” measure does not appear to provide additional knowledge with respect to previously proposed measures

Business goals, user needs, and requirements: A problem frame-based view

Background: It is well known that the analysis of requirements involves several stakeholders and perspectives. Very often several points of view at different abstraction levels have to be taken into account: all these features make requirements analysis a complex task. Such intrinsic complexity makes it difficult to understand several of the basic concepts that underlie requirements engineering. Actually, there is some confusion \u2013especially in industry\u2013 about what really a user requirement is, what are the differences between user requirements and user needs, and what are their relationships with business processes. Objective: The paper aims at clarifying the aforementioned issues, by providing a systematic and clear method for establishing requirements hierarchies. Method: The problem of describing requirements hierarchies is tackled using the problem frames concepts and notation. A case study is used throughout the paper to illustrate the proposed approach. Results: The description of requirements at different levels of abstractions and requirements hierarchies are illustrated. The resulting models are coherent with the reference model for requirements specifications and the problem frames. An analysis process that is aware of the differences between user needs and requirements is also provided, to illustrate the process of refining high-level goals into requirements that can be satisfied by a hardware/software machine. Conclusions: The proposed method appears promising to model, study and evaluate the relationships between business processes and the strategies for achieving business goals based on the usage of information technology

Using Static Analysis and Static Measurement for Industrial Software Quality Evaluation

Business organizations that outsource software development need to evaluate the quality of the code delivered by suppliers. In this paper, we illustrate an experience in setting up and using a toolset for evaluating code quality for a company that outsources software development. The selected tools perform static code analysis and static measurement, and provide evidence of possible quality issues. To verify whether the issues reported by tools are associated to real problems, code inspections were carried out. The combination of automated analysis and inspections proved effective, in that several types of defects were identified. Based on our findings, the business company was able to learn what are the most frequent and dangerous types of defects that affect the acquired code: this knowledge is now being used on a regular basis to perform focused verification activities

A Report on Using Simplified Function Point Measurement Processes

Background: Function Point Analysis is widely used, especially to quantify the size of applications in the early stages of development, when effort estimates are needed. However, the measurement process is often too long or too expensive or requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified methods have been proposed to measure Function Points. Objectives: The work reported here concerns the experimentation of simplified functional size measurement methods in the sizing of both \u201ctraditional\u201d and real-time applications. The goal is to evaluate the accuracy of the sizing with respect to full-fledged Function Point Analysis. Method: A set of projects, which had already been measured by means of Function Point Analysis, have been measured using the NESMA and Early&Quick Function Points simplified processes: the resulting size measures were compared. Results: while NESMA indicative method appears to quite overestimate the size of the considered applications, the other methods provide much more accurate estimates of functional size. EQFP methods proved more accurate in estimating the size of non Real-Time applications, while the NESMA estimated method proved fairly good in estimating both Real-Time and non Real-Time applications. Conclusions: The results of the experiment reported here show that in general it is possible to size software via simplified measurement processes with an acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the internals of both data and operations

Using Functional Complexity Measures in Software Development Effort Estimation

Several definitions of measures that aim at representing the size of software requirements are currently available. These measures have gained a quite relevant role, since they are one of the few types of objective measures upon which effort estimation can be based. However, traditional Functional Size Measures do not take into account the amount and complexity of elaboration required, concentrating instead on the amount of data accessed or moved. This is a problem since the amount and complexity of the required data elaboration affect the implementation effort, but are not adequately represented by the current size measures, including the standardized ones. Recently, a few approaches to measuring aspects of user requirements that are supposed to be related with functional complexity and/or data elaboration have been proposed by researchers. In this paper, we take into consideration some of these proposed measures and compare them with respect to their ability to predict the development effort, especially when used in combination with measures of functional size. A few methods for estimating software development effort \u2013both based on model building and on analogy\u2013 are experimented with, using different types of functional size and elaboration complexity measures. All the most significant models obtained were based on a notion of computation density that is based on the number of computation flows in functional processes. When using estimation by analogy, considering functional complexity in the selection of analogue projects improved accuracy in all the evaluated cases. In conclusion, it appears that functional complexity is a factor that affects development effort; accordingly, whatever method is used for effort estimation, it is advisable to take functional complexity into due consideration

ICSEA 2021: the sixteenth international conference on software engineering advances

The Sixteenth International Conference on Software Engineering Advances (ICSEA 2021), held on October 3 - 7, 2021 in Barcelona, Spain, 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. The tracks treated the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learnt. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. The conference had the following tracks: Advances in fundamentals for software development Advanced mechanisms for software development Advanced design tools for developing software Software engineering for service computing (SOA and Cloud) Advanced facilities for accessing software Software performance Software security, privacy, safeness Advances in software testing Specialized software advanced applications Web Accessibility Open source software Agile and Lean approaches in software engineering Software deployment and maintenance Software engineering techniques, metrics, and formalisms Software economics, adoption, and education Business technology Improving productivity in research on software engineering Trends and achievements Similar to the previous edition, this event continued to be very competitive in its selection process and very well perceived by the international software engineering community. As such, it is attracting excellent contributions and active participation from all over the world. We were very pleased to receive a large amount of top quality contributions. We take here the opportunity to warmly thank all the members of the ICSEA 2021 technical program committee as well as the numerous reviewers. The creation of such a broad and high quality conference program would not have been possible without their involvement. We also kindly thank all the authors that dedicated much of their time and efforts to contribute to the ICSEA 2021. We truly believe that thanks to all these efforts, the final conference program consists of top quality contributions. This event could also not have been a reality without the support of many individuals, organizations and sponsors. We also gratefully thank the members of the ICSEA 2021 organizing committee for their help in handling the logistics and for their work that is making this professional meeting a success. We hope the ICSEA 2021 was a successful international forum for the exchange of ideas and results between academia and industry and to promote further progress in software engineering research

An Empirical Evaluation of Simplified Function Point Measurement Processes

Function Point Analysis is widely used, especially to quantify the size of applications in the early stages of development, when effort estimates are needed. However, the measurement process is often too long or too expensive, or it requires more knowledge than available when development effort estimates are due. To overcome these problems, simplified methods have been proposed to measure Function Points. We used simplified methods for sizing both \u201ctraditional\u201d and Real-Time applications, with the aim of evaluating the accuracy of the sizing with respect to full-fledged Function PointAnalysis. To this end, a set of projects, which had already been measured by means of Function Point Analysis, have been measured using a few simplified processes, including those proposed by NESMA, the Early&Quick Function Points, the ISBSG average weights, and others; the resulting size measures were then compared. We also derived simplified size models by analyzing the dataset used for experimentations. In general, all the methods that provide predefined weights for all the transaction and data types identified in Function Point Analysis provided similar results, characterized by acceptable accuracy. On the contrary, methods that rely on just one of the elements that contribute to size tend to be quite inaccurate. In general, different methods show different accuracy for Real-Time and non Real-Time applications. The results of the analysis reported here show that in general it is possible to size software via simplified measurement processes with an acceptable accuracy. In particular, the simplification of the measurement process allows the measurer to skip the function weighting phases, which are usually expensive, since they require a thorough analysis of the details of both data and operations. Deriving our own models from the project datasets proved possible, and yielded results that are similar to those obtained via the methods proposed in the literature