Abstract

The paper demonstrates a chatbot used for elicitation of contextual information. The chatbot is an open-source application under GNU GPLv3. The project files of the chatbot are accessible at https://github.com/RWolfing/BugBot. A demonstration of the tool can be watched at https://youtu.be/a2gSBaijiY8.

Abstract

[Context and motivation] To validate developers’ ideas of what users might want and to understand user needs, it has been proposed to collect and combine system monitoring with user feedback. [Question/problem] So far, the monitoring data and feedback have been collected passively, hoping for the users to get active when problems emerge. This approach leaves unexplored opportunities for system improvement when users are also passive or do not know that they are invited to offer feedback. [Principal ideas/results] In this paper, we show how we have used goal monitors to identify interesting situations of system use and let a system autonomously elicit user feedback in these situations. We have used a monitor to detect interesting situations in the use of a system and issued automated requests for user feedback to interpret the monitoring observations from the users’ perspectives. [Contribution] The paper describes the implementation of our approach in a Smart City system and reports our results and experiences. It shows that combining system monitoring with proactive, autonomous feedback collection was useful and surfaced knowledge of system use that was relevant for system maintenance and evolution. The results were helpful for the city to adapt and improve the Smart City application and to maintain their internet-of-things deployment of sensors.

Abstract

Context: Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation.

Abstract

Crowd-based requirements engineering (CrowdRE) is promising to derive requirements by gathering and analyzing information from the crowd. Setting up CrowdRE in practice seems challenging, although first solutions to support CrowdRE exist. In this paper,we report on a German software company's experience on crowd involvement by using feedback communication channels and a monitoring solution for user-event data. In our case study, we identified several problem areas that a software company is confronted with to setup an environment for gathering requirements from the crowd. We conclude that a CrowdRE process cannot be implemented ad-hoc and that future work is needed to create and analyze a continuous feedback and monitoring data stream.

Abstract

End-user feedback is becoming more important for the evolution of software systems. There exist various communication channels for end-users (app stores, social networks) which allow them to express their experiences and requirements regarding a software application. End-users communicate a large amount of feedback via these channels which leads to open issues regarding the use of end-user feedback for software development, maintenance and evolution. This includes investigating how to identify relevant feedback scattered across different feedback channels and how to determine the priority of the feedback issues communicated. In this research preview paper, we discuss ideas for enduser driven feedback prioritization.

Abstract

Collecting and using user feedback as a method to support requirements engineering, might undermine user rights. This becomes apparent when looking at related areas, eg, research in user experience, where collecting user feedback also plays an important role. In such settings, researchers need to ensure that the stakeholders' rights and integrity are respected. This paper identifies and discusses some of the ethical challenges and issues a researcher can face, using an example case. Focusing on user feedback, this case can serve as an example for CrowdRE, ie several of our findings might apply to CrowdRE in general. However, further research is needed as our work mainly reflects the challenges experienced by the authors of this paper.

Abstract

Feedback communication channels allow end-users to express their needs, which can be considered in software development and evolution. Although feedback gathering and analysis have been identified as an important topic and several researchers have started their investigation, information is scarce on how software companies currently elicit end-user feedback. In this study, we explore the experiences of software companies with respect to feedback gathering. The results of a case study and online survey indicate two sides of the same coin: on the one hand, most software companies are aware of the relevance of end-user feedback for software evolution and provide feedback channels, which allow end-users to communicate their needs and problems. On the other hand, the quantity and quality of the feedback received varies. We conclude that software companies still do not fully exploit …

Abstract

Evolution of a software product is inevitable as product context changes and the product gradually becomes less useful if it is not adapted. Planning is a basis to evolve a software product. The product manager, who carries responsibilities of planning, requires but does not always have access to high-quality information for making the best possible planning decisions. The current study aims to understand whether and when analytics are valuable for product planning and how they can be interpreted to a software product plan. The study was designed with an interview-based survey methodology approach through 17 in-depth semi-structured interviews with product managers. Based on results from qualitative analysis of the interviews, we defined an analytics-based model. The model shows that analytics have potentials to support the interpretation of product goals while is constrained by both product characteristics and product goals. The model implies how to use analytics for a good support of product planning evolution.

Abstract

Quality requirements, an important class of non-functional requirements, are inherently difficult to elicit. Particularly challenging is the definition of good-enough quality. The problem cannot be avoided though, because hitting the right quality level is critical. Too little quality leads to churn for the software product. Excessive quality generates unnecessary cost and drains the resources of the operating platform. To address this problem, we propose to elicit the specific relationships between software quality characteristics and the impacts of potential quality levels. An understanding of each such relationship can then be used to specify the right quality level by deciding about acceptable impacts. This paper describes an approach to elicit such quality-impact relationships and use them for specifying quality requirements. The approach has been applied with user representatives in requirements workshops and used for determining Quality of Service (QoS) requirements based the involved users' Quality of Experience (QoE). The paper describes the approach in detail and reports experiences from applying the approach in software projects.

Abstract

Quality requirements, an important class of non-functional requirements, are inherently difficult to elicit. Particularly challenging is the definition of good-enough quality. The problem cannot be avoided though, because hitting the right quality level is critical. Too little quality leads to churn for the software product. Excessive quality generates unnecessary cost and drains the resources of the operating platform. To address this problem, we propose to elicit the specific relationships between software quality characteristics and the impacts of potential quality levels. An understanding of each such relationship can then be used to specify the right quality level by deciding about acceptable impacts. This paper describes an approach to elicit such quality-impact relationships and use them for specifying quality requirements. The approach has been applied with user representatives in requirements workshops and used for determining Quality of Service (QoS) requirements based the involved users' Quality of Experience (QoE). The paper describes the approach in detail and reports experiences from applying the approach in software projects.

Abstract

To create value with a software ecosystem (SECO), a platform owner has to ensure that the SECO is healthy and sustainable. Key Performance Indicators (KPI) are used to assess whether and how well such objectives are met and what the platform owner can do to improve. This paper gives an overview of existing research on KPI-based SECO assessment using a systematic mapping of research publications. The study identified 34 relevant publications for which KPI research and KPI practice were extracted and mapped. It describes the strengths and gaps of the research published so far, and describes what KPI are measured, analyzed, and used for decision-making from the researcher'st of view. For the researcher, the maps thus capture state-of-knowledge and can be used to plan further research. For practitioners, the generated map points to studies that describe how to use KPI for managing of a SECO.

Abstract

This work, which is connected to the Future Internet Public Private Partnership (FI-PPP) Integrated Project FI-STAR, presents a validation approach for Future Internet applications based on the use of analytics. In particular, it discusses how to use and combine software use and health statistics for the assessment of user-perceived Quality of Experience, in order to monitor user satisfaction, the risk of user churn, and the status of the corresponding ecosystem.

Abstract

SaaS cloud computing, in contrast to packaged products, enables permanent contact between users of a software product and the product-owning company. When planning the development and evolution of a software product, a product manager depends on reliable information about feature attractiveness. So far, planning decisions were based on stakeholder opinion and the customer's willingness to buy. Whether or not a feature actually is used was out of consideration. Analytics that measure the interaction between users and the SaaS gives product managers unprecedented access to information about product usage. To understand whether and how SaaS analytics can be used for product planning decision, we performed 17 in-depth interviews with experienced managers of SaaS products and analyzed the results analyzed with a mixed-method strategy. The empirical results characterize the relevance of a broad range of analytics for product planning decisions, and the strengths and limitations of an analytics-based product planning approach.

Abstract

Context. Successful software product management concerns about developing right software products for right markets at the right time. The product manager, who carries responsibilities of planning, requires but does not always have access to high-quality information for making the best possible planning decisions. The following master thesis concentrates on proposing a solution that supports planning of a software product by means of analytics.

Objectives. The aim of the master thesis is to understand potentials of analytics in product planning decisions in a SaaS context. This thesis focuses on SaaS based analytics used for portfolio management, product roadmapping, and release planning and specify how the analytics can be utilized for planning of a software product. Then the study devises an analytics-based method to enable software product planning.

Methods. The current study was designed with a mixed methodology approach, which includes the literature review and survey researches as well as case study under the framework of the design science. Literature review was conducted to identify product planning decisions and the measurements that support them. A total of 17 interview based surveys were conducted to investigate the impact of analytics on product planning decisions in product roadmapping context. The result of the interviews ended in an analytics-based planning method provided under the framework of design science. The designed analytics-based method was validated by a case study in order to measure the effectiveness of the solution.

Results. The identified product planning decisions were summarized and categorized into a taxonomy of decisions divided by portfolio management, roadmapping, and release planning. The identified SaaS-based measurements were categorized into six categories and made a taxonomy of measurements. The result of the survey illustrated that importance functions of the measurement- categories are not much different for planning-decisions. In the interviews 61.8% of interviewees selected �very important� for �Product�, 58.8% for �Feature�, and 64.7% for �Product healthiness� categories. For �Referral sources� category, 61.8% of responses have valuated as �not important�. Categories of �Technologies and Channels� and �Usage Pattern� have been rated majorly �important� by 47.1% and 32.4% of the corresponding responses. Also the results showed that product use, feature use, users of feature use, response time, product errors, and downtime are the first top measurement- attributes that a product manager prefers to use for product planning. Qualitative results identified �product specification, product maturity and goal� as the effected factors on analytics importance for product planning and in parallel specified strengths and weaknesses of analytical planning from product managers� perspectives. Analytics-based product planning method was developed with eleven main process steps, using the measurements and measurement scores resulted from the interviews, and finally got validated in a case. The method can support all three assets of product planning (portfolio management, roadmapping, and release planning), however it was validated only for roadmapping decisions in the current study. SaaS-based analytics are enablers for the method, but there might be some other analytics that can assist to take planning decisions as well.

Conclusion. The results of the interviews on the roadmapping decisions indicated that different planning decisions consider similar importance for measurement-categories to plan a software product. Statistics about feature use, product use, response time, users, error and downtime have been recognized as the most important measurements for planning. Analytics increase knowledge about product usability and functionality, and also can assist to improve problem handling and client-side technologies. But it has limitations regarding to receiving formed-based customer feedback, handling development technologies and also interpreting some measurements in practice. Immature products are not able to use analytics. To create, remove, or enhance a feature, the data trend provides a wide view of feature desirability in the current or even future time and clarifies how these changes can impact decision making. Prioritizing features can be performed for the features in the same context by comparing their measurement impacts. The analytics-based method covers both reactive and proactive planning.

Abstract

This paper presents an application of genetic algorithm for the problem of finding a specific layout of objects in addition to classify the layout. In other words, it combines the optimization capabilities of a genetic algorithm with classification capability of the k nearest neighbour's algorithm in layout analysis. We try to classify the layout in order to find the most appropriate layout (in terms of profitability). This paper focuses on the representation issues of the problem and on designing of the operators.

Abstract

Machine Translation is an applicable and developing topic to utilize artificial intelligence techniques. There are some automatic methods for evaluating machine translation results. These methods calculate scores to measures the desirability of the translation, by comparing a candidate translation with a reference one translated by human. However these evaluation methods have not reached to the accepted level of satisfaction yet. Also the parameters that human use in their translations, have not been incorporated in automatic machine translation evaluation. In this study, we apply a learning model using SVM classification; which can evaluate the translated sentences for fluency and adequacy. These parameters (fluency and adequacy) are being incorporated in the automatic machine translation evaluation. Results demonstrate that the proposed model improves the previous automatic MT evaluation methods at the sentence level.