The research examines the value and potential usefulness of using systemic thinking, which looks at the interconnectedness of things, to comprehend the complexities of software development projects and the technical and human factors involved. It considers two different aspects of systemic thinking - psychological and sociological - and posits that these can assist in understanding how software teams function and attain their objectives, as well as the goals of the entities for which they work. Our research aims to provide a novel contribution to the field by investigating the use of systemic thinking in software development teams and organizations. We evaluate the reliability and validity of the survey applied to different groups of relevant participants, relate our findings to existing literature, and identify the most representative factors of systemic thinking. Despite the popularity of various factors that fall under the umbrella of ’systems thinking’, there is limited understanding of their effectiveness in improving organizational performance or productivity, particularly when it comes to psychological and sociological systemic factors. The relationship between the use of systems thinking and organizational performance is often based on anecdotal evidence, rather than the identification and application of specific factors. Our work emphasizes the importance of understanding and applying such factors in order to build a solid foundation for the effective use of system dynamics and systems thinking tools, which is crucial for software development teams.

A programmers mind and mental state are valuable ingredients in software development process. Burnouts and stress are the biggest challenges software developers face in the current age. These challenges has been increasing at a fast rate together with the demand in more reliable software products. Various ways to relieve stress have been adopted and some of them result in autonomous sensory meridian response (ASMR). In this study we present a systematic literature review (SLR) focused on understanding the impact of ASMR in software development related activities and how it can be identified from brain waves using electroencephalography (EEG). From the SLR, we found that there exist a positive correlation between an ASMR therapy and alleviating state anxiety, which also it is suggested that ASMR may involve atypical attention processing. Secondly, the identification of specific brain signals patterns when a person is experiencing ASMR is possible using EEG. However a specific effect on software development related activities and software product is not presented in a scientific literature. Lastly, we found out that there is a need to conduct experiments focused on understanding the effect ASMR has on software development and programmers productivity or creativity. As a result, this work serves as a bases for conducting experiments focused in analyzing the mental states evoked by ASMR and the impact in the software engineering domain.

Automatically prioritizing software development tasks extracted from codes could provide significant technical and organizational advantages. Tools exist for the automatic extraction of tasks, but they still lack the ability to capture their mutual dependencies; hence, the capability to prioritize them. Solving this important puzzle is the goal of the presented industrial challenge.

Simple detection of bugs, defects or anomalies during software development is not enough - it is necessary to apply corrective actions to eliminate them. To find out whether an anomaly exists in any software, we can measure the quality attributes using software metrics. The main goal of this paper was to find out and explain how to meaningfully attribute metrics to useful corrective actions.

Ample research has been carried out in the area of collusion, plagiarism and e-learning. Collusion is a form of active cheating where two or more parties secretly or illegally corporate. Collusion is at the root of common knowledge plagiarism. While plagiarism requires two or more entities to compare, collusion can be determined in isolation. It is also possible that collusion do not lead to positive plagiarism checks. It is therefore the aims of this preliminary study to: (i) identify the factors responsible for collusion in e-learning (ii) determine the prominent factor that is representative of collusion and (iii) through user behaviour including, but not limited to, application switching time, determine collusion. We claim that user computer activities and application processes can help understand user behaviour during assessment task. It is on this premise that we develop a machine learning model to predict collusion through user behaviour during assessment task

Complex problems involve a concerted effort by the software team and can absorb vital resources, but our understanding of how the software team forms and succeeds has been minimal. It is not possible to explain the relationships between team achievement and scale, concentration, and especially team expertise by confound- ing elements, such as age group, additional participation from other individuals who are not in the team, or by team structures. This generates a need to understand software teams using systemic theory. This position paper presents the efforts we have undertaken to study the impact of systemic factors on software development teams and how systemic theory can be used to understand software teams. Our approach looks at the effect of psychological and sociological systemic variables on software teams to identify a way to represent software teams as systems

Modern Code Reviewing has shown to be an effective mechanism to identify bugs in the code; however, given their intrinsic subjectivity, they can be significantly affected by human factors such as interpersonal relationships. This paper focuses on exploring such issues, with specific attention to social iterations and personal factors. Future work includes experimental evaluations to verify the research hypothesis related to improving the quality of the process under the study.

Developers are indeed the most important resource in software production, and the individual developers are hard to substitute. The core of the work of the developers of knowledge intensive systems is in their mind, and this now a growing interest in understanding how to detect and model the state of their mind. Such analysis would enable to determine and model the optimal situation to develop in terms, for instance, of speed of development, minimization of errors, etc., and, to the extent possible, to recreate or to get close to such situation while organizing the work, the processes, the tools, and so on. The problem of performing such analysis is that the most refined equipment to model the work of the mind, like the fMRI, is very expensive and not movable. However, a tool, MNE, has been developed who is able to recreate accurate approximations using EEG of the data coming from an accurate wearable device that would come from the fMRI. This is a major enabler of the research in this area and in this paper details on why to select it and how to use it are provided. Current paper makes proof-of-concept to show that EEG is applicable for for retrieving information about functional state of the brain especially with a help of MNE tool.

Researchers have largely identified that interactions and communications pose major challenges in software development, especially when extracting requirements. However, they have not appreciated the sources and the depth of them, thus approaching them with mechanisms that have not (fully) achieved the desired objectives. In this position, we claim that such challenges can be explained using three major theories coming from social sciences: the theory of verbal and nonverbal communication, systemic theory, and democratic theory. We also argue that some of the successful practices of agile methods can be explained in terms of these theories. Finally, we stipulate that a full appreciation of these theories can result in a significant leap forward in the discipline, identifying new mechanisms that can help to overcome the mentioned challenges, understanding fully what we are doing and why

The use of biological signals to understand software development has become more popular in the last few years but poses new challenges with respect to the overall experimental settings.In this paper we present such challenges and the approach we took to overcome them.We illustrate our approach by evaluating two programming situations: pair programming and programming with music.The subjects involved in the experimentation are mostly students, however, in the largest case we involved graduate students coming from industry with at least three years of working experience.The results in general support the validity of this approach and encourage to go further in this research line.Moreover, as a byproduct, the analysis of pair programming confirms, from a biological perspective, early hypotheses that pair programming induces higher level of concentration.

The software is often produced under significant time constraints. Our idea is to understand the effects of various software development practices on the performance of developers working in stressful environments, and identify the best operating conditions for software developed under stressful conditions collecting data through questionnaires, non-invasive software measurement tools that can collect measurable data about software engineers and the software they develop, without intervening their activities, and biophysical sensors and then try to recreated also in different processes or key development practices such conditions.

Despite increasing popularity of developer dashboards, the effectiveness of dashboards is still in question. In order to design a dashboard that is effective and useful for developers, it is important to know (a) what information developers need to see in a dashboard, and (b) how developers want to use a dashboard with that necessary information. To answer these questions, we conducted two series of face-to-face individual interviews with developers. In the first step we analyzed answers, build a Goal-Question-Metric model and designed a precooked developer dashboard. Then, during the second separate series of interviews, we validated the GQM and derived feedback on the designed dashboard. Given that the cost of dashboard customization prevents developers from utilizing dashboards, we believe that our findings can provide a solid starting point to build precooked developer dashboards that can be readily utilized by software companies.

Software engineering education and training has obstacles caused by a lack of basic knowledge about a process of program exe- cution. The article is devoted to the development of special tools that help to visualize the process. We analyze existing tools and propose a new approach to stack and heap visualization. The solution is able to overcome major drawbacks of existing tools and suites well for analysis of programs written in Java and C/C++.

It is a cliche to say that there is a gap between research and practice. As the interest and importance in the practical impact of research has been growing, the gap between research and practice is expected to be narrowing. However, our study reveals that there still seems to be a wide gap. We survey so ware engineers about what they care about when developing so ware. We then compare our survey results with the research topics of the papers published in ICSE/FSE recently. We found the following discrepancy: while so ware engineers care more about so ware development productivity than the quality of so ware, papers on research areas closely related to so ware productivity--such as so ware development process management and so ware development techniques--are significantly less published than papers on so ware verification and validation that account for more than half of publications. We also found that so ware engineers are in great need for techniques for accurate effort estimation, and they are not necessarily knowledgable about techniques they can use to meet their needs.

The reputation of lightweight software development processes such as Agile and Lean is damaged by practitioners that claim benefits of such processes that are not true. Teams that want to demonstrate their seriousness, could benefit from matching their processes to the CMMI model, a recognized model by industry and the public administration. CMMI stands for Capability Maturity Model Integration and provides a reference model to improve and evaluate processes according to their maturity based on best practices. On the other hand, particularly in a lightweight software development process, the costs of a CMMI appraisal are hard to justify since its advantages are not directly related to the creation of value for the customer. This paper presents Jidoka4CMMI, a tool that — once a CMMI appraisal has been conducted — allows the documentation of the assessment criteria in form of executable test cases. The test cases, and so the CMMI appraisal, can be repeated anytime, without additional costs. The use of Jidoka4CMMI increases the benefits of conducting a CMMI appraisal. We hope that this encourages practitioners using lightweight software development processes to assess their processes using a CMMI model.

Web Services (WS) are software components accessible over the Internet through a well-defined set of standards. When consumers invoke a service, they expect to receive a valid response. However, the problem is to determine the structure of a valid request [21]. WS specifications are used to solve this problem since they are considered the primary piece of information for building service requests. Unfortunately, existing specifications do not provide enough support for this type information (e.g., WSDL) or there is little support on the client side (e.g., OWL-S). In this paper we address this issue by implementing a technique to reduce the number of faulty requests. We specifically propose an approach for extending WSDL with service input parameters rules that help consumers and integrators to verify their calls on the client side.

Can the methods of empirical software engineering give us answers to the truly important open questions in the field?

ABSTRACT This paper describes the challenges of handling changing requirements in software companies. This empirical investigation deals with the different sources of changes and with the different approaches to requirements evolution. We have considered the point of view of 35 managers of software companies interviewed through a questionnaire. The final results highlight some areas to improve requirements engineering and requirements management. Copyright © 2013 John Wiley & Sons, Ltd.

In the last decade, there has been increasing interest in pair programming (PP). However, despite the existing work, there is still a lack of substantial evidence of the effects of PP in industrial environments. To address this issue, we have analyzed the work of a team of 17 industrial developers for 14 months. The team is part of the IT department of a large Italian manufacturing company; it adopts a customized version of extreme programming (XP). We have investigated the effects of PP on software quality in five different scenarios. The results show that PP appears to provide a perceivable but small effect on the reduction of defects in these settings.

Pair Programming is one of the most studied and debated development techniques. However, at present, we do not have a clear, objective, and quantitative understanding of the claimed benefits of such development approach. All the available studies focus on the analysis of the effects of Pair Programming (e.g., code quality, development speed, etc.) with different findings and limited replicability of the experiments. This paper adopts a different approach that could be replicated in an easier way: it investigates how Pair Programming affects the way developers write code and interact with their development machine. In particular, the paper focuses on the effects that Pair Programming has on developers' attention and productivity. The study was performed on a professional development team observed for ten months and it finds out that Pair Programming helps developers to eliminate distracting activities and to focus on productive activities.

Developers use tools to develop software systems and always alleged better tools are being produced and purchased. Still there have been only limited studies on how people really use tools; these studies have used limited data, and the interactions between tools have not been properly elaborated. The advent of the AISEMA (Automated In-Process Software Engineering Measurement and Analysis) systems [3] has enabled a more detailed collection of tools data. Our "new idea" is to take advantage of such data to build a simple model based on an oriented graph that enables a good understanding on how tools are used individually and collectively. We have empirically validated the model analyzing an industrial team of 19 developers for a period of 10 months.

Novices' activities can be very different from those of experienced developers. The analysis of tool usage can propose early insights on novices integration in the team and also provide a method to compare their activities with experts. In this work we compare novices and experts in terms of tool usage, both in solo and in Pair Programming (PP). At the end of the integration process, novices activities are aligned to experts ones. Data for this study have been collected in an industrial team of 17 developers for a period of 10 months. The usage of AISEMA (Automated In-Process Software Engineering Measurement and Analysis) systems allowed us to have continuous, accurate and low cost measurement for our analysis.

In this paper we present a study on how pair programming (PP) facilitates the introduction of new developers (novices) in a team. Data has been collected non-invasively on how people paired in an industrial team of developers for 10 months; during such time novices joined the team. We focused on spontaneous PP, that is, on PP occurring when developers think it is most effective. Social network analysis techniques are used to analyze developers interactions and infer possible conclusions. It appears that initially PP is used to initiate novices, then it is drastically reduced to be resumed eventually when novices ldquofeelrdquo they have reached a significant level of maturity in the team. These phases are coherent with available works on group development and confirm existing findings on the importance of PP in the introduction of novices in teams, and, more generally, on team dynamics.

Automated systems for measurement and analysis are not adopted on a large scale in companies, despite the opportunities they offer. The fear of the ldquobig brotherrdquo and the lack of reports giving insights into the real adoption process and concrete usages in industry are barriers to this adoption. We report on a case-study on the adoption and long-term usage (2 years of running system) of such a system in a company focusing on the adoption process and the related challenges we encountered.

In this paper, we describe an experiment, which analyzes the relative importance and stability of change metrics for predicting defects for 3 releases of the Eclipse project. The results indicate that out of 18 change metrics 3 metrics contain most information about software defects. Moreover, those 3 metrics remain stable across 3 releases of the Eclipse project. A comparative analysis with the full model shows that the prediction accuracy is not too much affected by using a subset of 3 metrics and the recall even improves.

In this paper we present a comparative analysis of the predictive power of two different sets of metrics for defect prediction. We choose one set of product related and one set of process related software metrics and use them for classifying Java files of the Eclipse project as defective respective defect-free. Classification models are built using three common machine learners: logistic regression, Naïve Bayes, and decision trees. To allow different costs for prediction errors we perform cost-sensitive classification, which proves to be very successful: >75% percentage of correctly classified files, a recall of >80%, and a false positive rate <30%. Results indicate that for the Eclipse data, process metrics are more efficient defect predictors than code metrics.

In this demo, we present novel features added to the Moodle Learning Management System to achieve collaborative learning

Estimation of development effort without imposing overhead on the project and the development team is of paramount importance for any software company. This study proposes a new effort estimation methodology aimed at agile and iterative development environments not suitable for description by traditional prediction methods. We propose a detailed development methodology, discuss a number of architectures of such models (including a wealth of augmented regression models and neural networks) and include a thorough case study of Extreme Programming (XP) in two semi-industrial projects. The results of this research evidence that in the XP environment under study the proposed incremental model outperforms traditional estimation techniques most notably in early phases of development. Moreover, when dealing with new projects, the incremental model can be developed from scratch without resorting itself to historic data.

The development of a software project often requires more time and effort than originally expected. However, at end of a project, it is hard to determine which components required more time or were more complex than what was originally planned. In addition, project managers are interested to know what could have happened if some requirements were dropped or if they were implemented in different time sequencing. Lagrein is a tool that supports managers and developers in answering such kind of questions.

This paper presents an empirical analysis of the Open Source development process from the point of view of the involvement of the developers in the production process. The study focuses on how developers contribute to projects in terms of involvement, size and kind of their contribution. Data have been collected from 53 Open Source projects and target application domains include different areas: web and application servers, databases, operating systems, and window managers. Collected data include the number of developers, patterns of code modifications, and evolution over the time of size and complexity. The results of this study show evidence that there are recurrent patterns in Open Source software development and these patterns are common to all the projects considered even if there are no superimposed processes for development, application domains are different, and there are contributions from people spread across the world.

We describe an empirical study of open-source and closed-source software projects. The motivation for this research is to quantitatively investigate common perceptions about open-source projects, and to validate these perceptions through an empirical study. We investigate the hypothesis that open-source software grows more quickly, but does not find evidence to support this. The project growth is similar for all the projects in the analysis, indicating that other factors may limit growth. The hypothesis that creativity is more prevalent in open-source software is also examined, and evidence to support this hypothesis is found using the metric of functions added over time. The concept of open-source projects succeeding because of their simplicity is not supported by the analysis, nor is the hypothesis of open-source projects being more modular. However, the belief that defects are found and fixed more rapidly in open-source projects is supported by an analysis of the functions modified. We find support for two of the five common beliefs and conclude that, when implementing or switching to the open-source development model, practitioners should ensure that an appropriate metrics collection strategy is in place to verify the perceived benefits.

No abstract available.

Software measures (metrics) provide software engineers with an important means of quantifying essential features of software products and software processes such as software reliability, maintenance, reusability and alike. Software measures interact between themselves. Some of them may be deemed redundant. Software measures are used to construct detailed prediction models. The objective of this study is to pursue an association analysis of software measures by revealing dependencies (associations) between them. More specifically, the introduced association analysis is carried out at the local level by studying dependencies between information granules of the software measures. This approach is contrasted with a global level such as e.g., regression analysis. We discuss the role of information granules as meaningful conceptual entities that facilitate analysis and give rise to a user-friendly, highly transparent environment.

This study is concerned with the Boolean satisfiability (SAT) problem and its solution in setting a hybrid computational intelligence environment of genetic and fuzzy computing. In this framework, fuzzy sets realize an embedding principle meaning that original two-valued (Boolean) functions under investigation are extended to their continuous counterparts resulting in the form of fuzzy (multivalued) functions. In the sequel, the SAT problem is reformulated for the fuzzy functions and solved using a genetic algorithm (GA). It is shown that a GA, especially its recursive version, is an efficient tool for handling multivariable SAT problems. Thorough experiments revealed that the recursive version of the GA can solve SAT problems with more than 1000 variables.

S.753-754 : Ill.

Holmes is a software product line tool that supports all core activities of software product line analysis and development. Holmes integrates its tools using a blackboard architecture based on a Linda tuple space. A novel feature is the use of a critiquing system to provide semantic support. This is demonstrated with an example.

This paper reports on empirical research based on two software products. The research goal is to ascertain the impact of the adoption of a reuse policy on customer satisfaction. The results show that when a systematic reuse policy is implemented, such as the adoption of a domain specific library: reuse is significantly positively correlated with customer satisfaction; and there is a significant increase in customer satisfaction. The results have been extended to the underlying populations, supposed normal.

This work reports on a study on the effectiveness of the usage of Internet based facilities in 4 graduate and undergraduate classes in software engineering. In the framework of this study it appears that the electronic communication media in engineering classes is effective if used as a complement to the traditional "face-to-face" communication, rather then as a substitute to it. Decision making activities are more effective if performed face-to-face, while Internet is more convenient for task and process oriented activities. Also, differences in the appreciation of Internet exist between graduate and undergraduate students.

No abstract available.

This distribution is counter-intuitive for at least two reasons. First it would seem “obvious” that the numbers drawn from a list generated from widely different arbitrary processes would have roughly equally probabilities for the digits 1 and 9 to be first digits. This is not normally the case. If the list of numbers does not have artificial limits, or include invented numbers such as postal codes, then approximately 30% of the numbers will have 1 as their first digit, but only 5% will have 9 as their first digit. Deviations from the expected Benford Distribution indicate the presence of some special characteristic of the data. The second, more theoretically challenging, problem is: What is the underlying property associated with so many widely different processes which generates lists of numbers that follow Benford's Law?

The Internet creates new opportunities for component distribution. Infrastructure for dynamic, Web-based composition of software components appears to be a very impelling need. This demonstration focuses on a Web-based system that supports dynamic component composition.

No abstract available.

Software product lines are one of the most promising fields in software engineering. They aim at the synergistic construction of software products. A successful introduction of software product lines requires three essential ingredients: a business analysis of the overall advantages that can come from product lines, the definition of a systematic process for product lines development, and the definition of general models, in a standard format, which can guide the development process.

The MILOS system supports software development processes over the Internet.It integrates process modeling with project planning and enactment.Our flexible workflow engine allows refining and changing process models during project execution, The built-in traceability component supports change notifications and helps the project participants to ensure that the project plan as well as the state of the enactment engine reflect the "real world development process.Tool integration is accomplished by using the built-in capabilities of Web browsers.

Although logic languages, due to their non-declarative nature, are widely proclaimed to be conducive in theory to parallel implementation, in fact there appears to be insufficient practical evidence to stimulate further developments in this field. The paper puts forward various complications which arise in assuming a solely process parallel approach as a possible explanation for this situation. As an alternative, data parallelism is posited as an underutilized forte of logic programming. The paper illustrates a data parallel implementation of a particular language called SEL which is based on sets. Thus, SEL (set equational language) is introduced as an example of logic language which lends itself to an efficient data parallel implementation. The strategy of this implementation assumes an abstract machine called SAM (set-oriented abstract machine) which is based on the WAM (Warren abstract machine). SAM serves as an intermediary between the SEL language and the target machine for the implementation, the Connection Machine. Finally, some preliminary benchmarks are presented.