Maleknaz Nayebi

Toward Data-Driven Requirements Engineering

Nowadays, users can easily submit feedback about software products in app stores, social media, or user groups. Moreover, software vendors are collecting massive amounts of implicit feedback in the form of usage data, error logs, and sensor data. These trends suggest a shift toward data-driven user-centered identification, prioritization, and management of software requirements. Developers should be able to adopt the requirements of masses of users when deciding what to develop and when to release. They could systematically use explicit and implicit user data in an aggregated form to support requirements decisions. The goal is data-driven requirements engineering by the masses and for the masses.

Release Practices for Mobile Apps -- What do Users and Developers Think?

Large software organizations such as Facebook or Netflix, who otherwise make daily or even hourly releases of their web applications using continuous delivery, have had to invest heavily into a customized release strategy for their mobile apps, because the vetting process of app stores introduces lag and uncertainty into the release process. Amidst these large, resourceful organizations, it is unknown how the average mobile app developer organizes her apps releases, even though an incorrect strategy might bring a premature app update to the market that drives away customers towards the heavy market competition. To understand the common release strategies used for mobile apps, the rationale behind them and their perceived impact on users, we performed two surveys with users and developers. We found that half of the developers have a clear strategy for their mobile app releases, since especially the more experienced developers believe that it affects user feedback. We also found that users are aware of new app updates, yet only half of the surveyed users enables automatic updating of apps. While the release date and frequency is not a decisive factor to install an app, users prefer to install apps that were updated more recently and less frequently. Our study suggests that an apps release strategy is a factor that affects the ongoing success of mobile apps.

An open innovation approach in support of product release decisions

Release decisions are of pivotal importance for product success in incremental and iterative software development. In this paper, the wickedness of these decisions is approached by a collective problem solving process. The paradigm of Open Innovations is emphasizing the range of opportunities available to get access to distributed knowledge and information. In particular, we apply (i) Analytical Open Innovation for information gathering and (ii) Morphological Analysis (MA) for problem structuring. The proposed decision support methodology is illustrated by a comprehensive case study. In the context of OTT service delivery, planning of both features and their different functionality levels is studied. From the broad involvement of stakeholders in the whole formulation, structuring and solution process, a higher validity and customer value of the developed products is demonstrated. Without performing MA, the proposed feature implementations would include inconsistencies and thus create customer and user concerns. Furthermore, from community based detection of cost and value synergies, potential resource savings and additional value creation opportunities are utilized.

Analytical Product Release Planning

As part of any incremental and iterative development, release planning is the process of assigning features to upcoming releases (or iterations) such that the overall product evolution is optimized. Analytical product release planning refers to the application of analytical methods in this process, thereby utilizing the diversity of data available from internal and external sources of information. In this chapter, information needs for release planning are outlined and a taxonomy of release planning problems is given. The paradigm of Open Innovation is introduced as a new way to elicit and gain access to relevant data related to product objectives, features and their dependencies, customers and changing priorities, as well as product values and market trends. Analytical Open Innovation (AOI) is the integration of Open Innovation with (a portfolio of) analytical methods which could be used in different problems of a semi-wicked nature such as planning and design. This chapter studies the usage of AOI in the context of release planning (RP). The respective approach called “AOI@RP” is taking advantage of gathering and generating data and relating data into well-defined aspects of the problem and combining analytical methods to address the solution. The usage of AOI is studied in more detail for two of the concrete release planning problems given in the taxonomy: (1) Release planning in the presence of advanced feature dependencies and synergies detected from morphological analysis; (2) continuous what-to-release planning in consideration of ongoing trial feature evaluation. An illustrative case study is used as proof of concept to the proposed solution methodology.

Analytical Open Innovation for Value-Optimized Service Portfolio Planning

Service portfolio planning is the process of designing collections of services and deciding on their provision. The problem is highly information and decision centric. In this paper, we present a solution approach called Analytical Open Innovation (AOI). Open innovation facilitates comprehensive crowdsourcing and social media information analysis. In our proposed approach (AOI), open innovation is utilized for the elicitation of service needs, the definition of quality provision levels for each of the services, and detection of service dependencies and cost and value synergies. As the result of a rigorous optimization process, diversified and resource-optimized service portfolios are created. As a proof of concept, the proposed approach is illustrated via a case study project using Over the Top TV (OTT) services to be offered at different levels of quality over four quarters of a year.

Analytics for Software Project Management -- Where are We and Where do We Go?

Software project management is a decision intensive process. Success or failure of the project is highly dependent on these decisions. Analytical techniques and tools can support project managers throughout the software project life cycle by increasing the predictability and chance of success in these projects. In this paper, we report the results of a systematic mapping study within which we investigate the usage of different types of analytics for software project management. We analyze the accessibility of the data as well as the degree of validation reported in the 115 studies selected for final analysis. This resulted in a picture of the status quo (Where are we?) of analytics in software project management. From comparing this status quo with the results of an industrial survey on the industrial needs of different types of analysis, we propose an agenda on future work (Where do we go?).

Trade-off Service Portfolio Planning - A Case Study on Mining the Android App Market

Service portfolio planning is the process of designing collections of services and deciding on their provision. The problem is highly information intensive, and most of the information required is hard to gather. In this paper, we present a solution approach based on the paradigm of Analytical Open Innovation (AOI). Open innovation is a cheap and low risk problem solving approach which relies on knowledge exchange with outside of company as a competitive advantage. Different forms of open innovation; crowd source, open source and outsource; facilitate the provider and consumer interactions and brings higher customer value. In our proposed AOI approach, open innovation is utilized for elicitation of services from web data, crowdsourcing the service value from potential customers and for the estimation of service implementation effort. For service evaluation, we apply the Kano theory of product development and customer satisfaction. Based on that and as the result from an optimization process, resource-optimized service portfolios are created that constitute trade-offs in balancing between gained value and effort needed. As a proof of concept, the proposed approach is illustrated via a case study project for the composition of Over the Top TV (OTT) services. The atomic services from 241 qualified apps were analyzed from the android app market. We demonstrate that the proposed approach is able to generate optimized trade-off solutions, composing better apps at each capacity level and achieving better customer satisfaction .The level of improvement in customer satisfaction varies between 16.5% and 95.3%.

A Two-staged Survey on Release Readiness

Deciding about the content and readiness when shipping a new product release can have a strong impact on the success (or failure) of the product. Having formerly analyzed the state-of-the art in this area, the objective for this paper was to better understand the process and rationale of real-world release decisions and to what extent research on release readiness is aligned with industrial needs. We designed two rounds of surveys with focus on the current (Survey-A) and the desired (Survey-B) process of how to make release readiness decisions. We received 49 and 40 valid responses for Survey-A and Survey-B, respectively. In total, we identified 12 main findings related to the process, the rationale and the tool support considered for making release readiness decisions. We found that reasons for failed releases and the factors considered for making release decisions are context specific and vary with release cycle time. Practitioners confirmed that (i) release readiness should be measured and continuously monitored during the whole release cycle, (ii) release readiness decisions are context-specific and should not be based solely on quality considerations, and iii) some of the observed reasons for failed releases such as low functionality, high cost, and immature service are not adequately studied in research where there is dominance on investigating quality and testing only. In terms of requested tool support, dashboards covering multidimensional aspects of the status of release development were articulated as key requirements.

App store mining is not enough for app improvement

The rise in popularity of mobile devices has led to a parallel growth in the size of the app store market, intriguing several research studies and commercial platforms on mining app stores. App store reviews are used to analyze different aspects of app development and evolution. However, app users’ feedback does not only exist on the app store. In fact, despite the large quantity of posts that are made daily on social media, the importance and value that these discussions provide remain mostly unused in the context of mobile app development. In this paper, we study how Twitter can provide complementary information to support mobile app development. By analyzing a total of 30,793 apps over a period of six weeks, we found strong correlations between the number of reviews and tweets for most apps. Moreover, through applying machine learning classifiers, topic modeling and subsequent crowd-sourcing, we successfully mined 22.4% additional feature requests and 12.89% additional bug reports from Twitter. We also found that 52.1% of all feature requests and bug reports were discussed on both tweets and reviews. In addition to finding common and unique information from Twitter and the app store, sentiment and content analysis were also performed for 70 randomly selected apps. From this, we found that tweets provided more critical and objective views on apps than reviews from the app store. These results show that app store review mining is indeed not enough; other information sources ultimately provide added value and information for app developers.

Optimized Functionality for Super Mobile Apps

Functionality of software products often does not match user needs and expectations. The closed set-up of systems and information is replaced by wide access to data of users and competitor products. This shift offers completely new opportunities to approach requirements elicitation and subsequent planning of software functionality. This is, in particular true for app store markets. App stores are markets for many small sized software products which provide an open platform for users to provide feedback on using apps. Moreover, the functionality and status of similar software products can be retrieved. While this is a competitive risk, it is at the same time an opportunity.In this paper, we envision a new release planning approach that leverages the new opportunities for decision making. We propose a new model using bi-criterion integer programming. We make suggestions for optimized super app functionality that are based on two key aspects: (i) the estimated value of features, and (ii) the cohesiveness between newly added features and cohesiveness between existing and the features to be added. The information on these attributes comes from reasoning on feature composition of existing similar apps. The approach is applicable to the development of new product releases as well as to the creation of completely new apps. We illustrate the applicability of our model by a small example and outline directions for future research.