Visualizing the customization endeavor in product-based-evolving software product lines: a case of action design research

[EN] Software Product Lines (SPLs) aim at systematically reusing software assets, and deriving products (a.k.a., variants) out of those assets. However, it is not always possible to handle SPL evolution directly through these reusable assets. Time-to-market pressure, expedited bug fixes, or product specifics lead to the evolution to first happen at the product level, and to be later merged back into the SPL platform where the core assets reside. This is referred to as product-based evolution. In this scenario, deciding when and what should go into the next SPL release is far from trivial. Distinct questions arise. How much effort are developers spending on product customization? Which are the most customized core assets? To which extent is the core asset code being reused for a given product? We refer to this endeavor as Customization Analysis, i.e., understanding the functional increments in adjusting products from the last SPL platform release. The scale of the SPLs' code-base calls for customization analysis to be conducted through Visual Analytics tools. This work addresses the design principles for such tools through a joint effort between academia and industry, specifically, Danfoss Drives, a company division in charge of the P400 SPL. Accordingly, we adopt an Action Design Research approach where answers are sought by interacting with the practitioners in the studied situations. We contribute by providing informed goals for customization analysis as well as an intervention in terms of a visual analytics tool. We conclude by discussing to what extent this experience can be generalized to product-based evolving SPL organizations other than Danfoss Drives.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is supported by the Spanish Ministry of Science, Innovation and Universities grant number RTI2018099818-B-I00 and MCIU-AEI TIN2017-90644-REDT (TASOVA). ONEKIN enjoys support from the program 'Grupos de Investigacion del Sistema Univesitario Vasco 2019-2021' under contract IT1235-19. Raul Medeiros enjoys a doctoral grant from the Spanish Ministry of Science and Innovation

Survey of Controlled Software Engineering Experiments with Focus on Subjects

Empirical research within the Software Engineering field is a fairly new discipline. This calls for the development of new methods and new standards for how to perform and report experimental procedures and results. For research to be of interest for the community we need to know how the research has been performed, and within which context. Through the CONTEXT project, employees and M.Sc. students at Simula Research Laboratories have performed a survey on all controlled experiments reported in 12 leading journals and conferences during the period 1993 to 2002; a total of 118 experiments from 107 papers. Our aim has been to find out how the experiments are reported – what information is supplied, and to some extent the quality of this information. The focus of this thesis is on the subjects participating in the experiments. What information is provided regarding who they are and their backgrounds, and to what extent variables that might be of relevance for the validity and generalizability are described. This gives an indication of the maturity of this research. Almost all controlled experiments in our field are aimed at the professional population, yet only 26 % of the experiments in our survey used professionals as subjects. Only 19 % of the experiments using students as subjects generalize their findings to professionals. The heterogeneity of the subjects is generally not given much attention, as differences between them are rarely discussed and background variables often described in little detail. We have found that whether or not it is appropriate to use students as subjects is highly dependent on the issue studied and which populations the research is targeting. The terms “student” and “professional” are in many cases misleading, as these may not be relevant for the task at hand. It may often make more sense to talk about novices and experts. The overall impression of controlled experiments in the SE field is that this discipline is developing, but not yet mature. Our analysis shows that there is a need for standards and guidelines that authors should adhere to when publishing their work. Today many papers provides so little information that it is difficult for the reader to get an impression of to what extent the results are valid, and if they are – to whom

Visualizing and Understanding Code Duplication in Large Software Systems

Code duplication, or code cloning, is a common phenomena in the development of large software systems. Developers have a love-hate relationship with cloning. On one hand, cloning speeds up the development process. On the other hand, clone management is a challenging task as software evolves. Cloning has commonly been considered as undesirable for software maintenance and several research efforts have been devoted to automatically detect clones and eliminate clones aggressively. However, there is little empirical work done to analyze the consequences of cloning with respect to the software quality. Recent studies show that cloning is not necessarily undesirable. Cloning can used to minimize risks and there are cases where cloning is used as a design technique. In this thesis, three visualization techniques are proposed to aid researchers in analyzing cloning in studying large software systems. All of the visualizations abstract and display cloning information at the subsystem level but with different emphases. At the subsystem level, clones can be classified as external clones and internal clones. External clones refer to code duplicates that reside in the same subsystem, whereas external clones are clones that are spread across different subsystems. Software architecture quality attributes such as cohesion and coupling are introduced to contribute to the study of cloning at the architecture level. The Clone Cohesion and Coupling (CCC) Graph and the Clone System Hierarchy (CSH) Graph display the cloning information for one single release. In particular, the CCC Graph highlights the amount of internal and external cloning for each subsystems; whereas the CSH Graph focuses more on the details of the spread of cloning. Finally, the Clone System Evolution (CSE) Graph shows the evolution of cloning over a period of time

On the application of artificial intelligence and human computation to the automation of agile software task effort estimation

Software effort estimation (SEE), as part of the wider project planning and product road mapping process, occurs throughout a software development life cycle. A variety of effort estimation methods have been proposed in the literature, including algorithmic methods, expert based methods, and more recently, methods based on techniques drawn from machine learning and natural language processing. In general, the consensus in the literature is that expert-based methods such as Planning Poker are more reliable than automated effort estimation. However, these methods are labour intensive and difficult to scale to large-scale projects. To address this limitation, this thesis investigates the feasibility of using human computation techniques to coordinate crowds of inexpert workers to predict expert-comparable effort estimates for a given software development task. The research followed an empirical methodology and used four different methods: literature review, replication, a series of laboratory experiments, and ethnography. The literature uncovered the lack of suitable datasets that include the attributes of descriptive text (corpus), actual cost, and expert estimates for a given software development task. Thus, a new dataset was developed to meet the necessary requirements. Next, effort estimation based on recent natural language processing advancements was evaluated and compared with expert estimates. The results suggest that there was no significant improvement, and the automated approach was still outperformed by expert estimates. Therefore, the feasibility of scaling the Planning Poker effort estimation method by using human computation in a micro-task crowdsourcing environment was explored. A series of pilot experiments were conducted to find the proper design for adapting Planning Poker to a crowd environment. This resulted in designing a new estimation method called Crowd Planning Poker (CPP). The pilot experiments revealed that a significant proportion of the crowd submitted poor quality assignments. Therefore, an approach to actively managing the quality of SEE work was proposed and evaluated before being integrated into the CPP method. A substantial overall evaluation was then conducted. The results demonstrated that crowd workers were able to discriminate between tasks of varying complexity and produce estimates that were comparable with those of experts and at substantially reduced cost compared with small teams of domain experts. It was further noted in the experiments that crowd workers provide useful insights as to the resolution of the task. Therefore, as a final step, fine-grained details about crowd workers’ behaviour, including actions taken and artifacts reviewed, were used in an ethnographic study to understand how crowd effort estimation takes place in a crowd. Four persona archetypes were developed to describe the crowd behaviours, and the results of the behaviour analysis were confirmed by surveying the crowd workers

Factors that Impact the Cloud Portability of Legacy Web Applications

The technological dependency of products or services provided by a particular cloud platform or provider (i.e. cloud vendor lock-in) leaves cloud users unprotected against service failures and providers going out of business, and unable to modernise their software applications by exploiting new technologies and cheaper services from alternative clouds. High portability is key to ensure a smooth migration of software applications between clouds, reducing the risk of vendor lock-in. This research identifies and models key factors that impact the portability of legacy web applications in cloud computing. Unlike existing cloud portability studies, we use a combination of techniques from empirical software engineering, software quality and areas related to cloud, including service-oriented computing and distributed systems, to carry out a rigorous experimental study of four factors impacting on cloud application portability. In addition, we exploit established methods for software effort prediction to build regression models for predicting the effort required to increase cloud application portability. Our results show that software coupling, authentication technology, cloud platform and service are statistically significant and scientifically relevant factors for cloud application portability in the experiments undertaken. Furthermore, the experimental data enabled the development of fair (mean magnitude of relative error, MMRE, between 0.493 and 0.875), good (MMRE between 0.386 and 0.493) and excellent (MMRE not exceeding 0.368) regression models for predicting the effort of increasing the portability of legacy cloud applications. By providing empirical evidence of factors that impact cloud application portability and building effort prediction models, our research contributes to improving decision making when migrating legacy applications between clouds, and to mitigating the risks associated with cloud vendor lock-in