arXiv subjects

Guenther Ruhe

Publications and source records attributed to Guenther Ruhe.

At least 19 recordsLinked to original sources

arXiv2024

Recommending and Release Planning of User-Driven Functionality Deletion for Mobile Apps

Evolving software with an increasing number of features poses challenges in terms of comprehensibility and usability. Traditional software release planning has predominantly focused on orchestrating the addition of features, contributing to the growing complexity and maintenance demands of larger software systems. In mobile apps, an excess of functionality can significantly impact usability, maintainability, and resource consumption, necessitating a nuanced understanding of the applicability of the law of continuous growth to mobile apps. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews. For most users, the removal of features is associated with negative sentiments, prompts changes in usage patterns, and may even result in user churn. Motivated by these preliminary results, we propose Radiation to input user reviews and recommend if any functionality should be deleted from an app's User Interface (UI). We evaluate radiation using historical data and survey developers' opinions. From the analysis of 190,062 reviews from 115 randomly selected apps, we show that Radiation can recommend functionality deletion with an average F-Score of 74% and if sufficiently many negative user reviews suggest so. We conducted a survey involving 141 software developers to gain insights into the decision-making process and the level of planning for feature deletions. Our findings indicate that 77.3% of the participants often or always plan for such deletions. This underscores the importance of incorporating feature deletion planning into the overall release decision-making process.

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arXiv2024

More Insight from Being More Focused: Analysis of Clustered Market Apps

The increasing attraction of mobile apps has inspired researchers to analyze apps from different perspectives. As with any software product, apps have different attributes such as size, content maturity, rating, category, or number of downloads. Current research studies mostly consider sampling across all apps. This often results in comparisons of apps being quite different in nature and category (games compared with weather and calendar apps), also being different in size and complexity. Similar to proprietary software and web-based services, more specific results can be expected from looking at more homogeneous samples as they can be received as a result of applying clustering. In this paper, we target homogeneous samples of apps to increase the degree of insight gained from analytics. As a proof-of-concept, we applied the clustering technique DBSCAN and subsequent correlation analysis between app attributes for a set of 940 open-source mobile apps from F-Droid. We showed that (i) clusters of apps with similar characteristics provided more insight compared to applying the same to the whole data and (ii) defining the similarity of apps based on the similarity of topics as created from the topic modeling technique Latent Dirichlet Allocation does not significantly improve clustering results.

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arXiv2024

Analysis of Marketed versus Not-marketed Mobile App Releases

Market and user characteristics of mobile apps make their release management different from proprietary software products and web services. Despite the wealth of information regarding users' feedback on an app, an in-depth analysis of app releases is difficult due to the inconsistency and uncertainty of the information. To better understand and potentially improve app release processes, we analyze major, minor, and patch releases for releases following semantic versioning. In particular, we were interested in finding out the difference between marketed and not-marketed releases. Our results show that, in general, major, minor, and patch releases have significant differences in the release cycle duration, nature, and change velocity. We also observed that there is a significant difference between marketed and non-marketed mobile app releases in terms of cycle duration, nature and the extent of changes, and the number of opened and closed issues.

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arXiv2023

User Driven Functionality Deletion for Mobile Apps

Evolving software with an increasing number of features is harder to understand and thus harder to use. Software release planning has been concerned with planning these additions. Moreover, software of increasing size takes more effort to be maintained. In the domain of mobile apps, too much functionality can easily impact usability, maintainability, and resource consumption. Hence, it is important to understand the extent to which the law of continuous growth applies to mobile apps. Previous work showed that the deletion of functionality is common and sometimes driven by user reviews. However, it is not known if these deletions are visible or important to the app users. In this study, we performed a survey study with 297 mobile app users to understand the significance of functionality deletion for them. Our results showed that for the majority of users, the deletion of features corresponds with negative sentiments and change in usage and even churn. Motivated by these preliminary results, we propose RADIATION to input user reviews and recommend if any functionality should be deleted from an app's User Interface (UI). We evaluate RADIATION using historical data and surveying developers' opinions. From the analysis of 190,062 reviews from 115 randomly selected apps, we show that RADIATION can recommend functionality deletion with an average F-Score of 74% and if sufficiently many negative user reviews suggest so.

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arXiv2022

An Empirical Study of Blockchain Repositories in GitHub

Blockchain is a distributed ledger technique that guarantees the traceability of transactions. Blockchain is adopted in multiple domains like finance (e.g., cryptocurrency), healthcare, security, and supply chain. In the open-source software (OSS) portal GitHub, we observe a growing adoption of Blockchain-based solutions. Given the rapid emergence of Blockchain-based solutions in our daily life and the evolving cryptocurrency market, it is important to know the status quo, how developers generally interact in those repos, and how much freedom they have in applying code changes. We report an empirical study of 3,664 Blockchain software repositories from GitHub. We divide the Blockchain repositories into two categories: Tool (e.g., SDKs) and Applications (e.g., service/solutions developed using SDKs). The Application category is further divided into two sub-categories: Crypto and Non-Crypto applications. In all Blockchain repository categories, the contribution interactions on commits are the most common interaction type. We found that more organizations contributing to the Blockchain repos than individual users. The median numbers of internal and external users in tools are higher than the application repos. We observed a higher degree of collaboration (e.g., for maintenance efforts) among users in Blockchain tools than those in the application repos. Among the artifacts, issues have a greater number of interactions than commits and pull requests. Related to autonomy we found that less than half of total project contributions are autonomous. Our findings offer implications to Blockchain stakeholders, like developers to stay aware of OSS practices around Blockchain software.

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arXiv2021

How Much Data Analytics is Enough? The ROI of Machine Learning Classification and its Application to Requirements Dependency Classification

Machine Learning (ML) can substantially improve the efficiency and effectiveness of organizations and is widely used for different purposes within Software Engineering. However, the selection and implementation of ML techniques rely almost exclusively on accuracy criteria. Thus, for organizations wishing to realize the benefits of ML investments, this narrow approach ignores crucial considerations around the anticipated costs of the ML activities across the ML lifecycle, while failing to account for the benefits that are likely to accrue from the proposed activity. We present findings for an approach that addresses this gap by enhancing the accuracy criterion with return on investment (ROI) considerations. Specifically, we analyze the performance of the two state-of-the-art ML techniques: Random Forest and Bidirectional Encoder Representations from Transformers (BERT), based on accuracy and ROI for two publicly available data sets. Specifically, we compare decision-making on requirements dependency extraction (i) exclusively based on accuracy and (ii) extended to include ROI analysis. As a result, we propose recommendations for selecting ML classification techniques based on the degree of training data used. Our findings indicate that considering ROI as additional criteria can drastically influence ML selection when compared to decisions based on accuracy as the sole criterion

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arXiv2021

An Evolutionary Algorithm for Task Scheduling in Crowdsourced Software Development

The complexity of software tasks and the uncertainty of crowd developer behaviors make it challenging to plan crowdsourced software development (CSD) projects. In a competitive crowdsourcing marketplace, competition for shared worker resources from multiple simultaneously open tasks adds another layer of uncertainty to the potential outcomes of software crowdsourcing. These factors lead to the need for supporting CSD managers with automated scheduling to improve the visibility and predictability of crowdsourcing processes and outcomes. To that end, this paper proposes an evolutionary algorithm-based task scheduling method for crowdsourced software development. The proposed evolutionary scheduling method uses a multiobjective genetic algorithm to recommend an optimal task start date. The method uses three fitness functions, based on project duration, task similarity, and task failure prediction, respectively. The task failure fitness function uses a neural network to predict the probability of task failure with respect to a specific task start date. The proposed method then recommends the best tasks start dates for the project as a whole and each individual task so as to achieve the lowest project failure ratio. Experimental results on 4 projects demonstrate that the proposed method has the potential to reduce project duration by a factor of 33-78%.

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arXiv2021

CrowdSim: A Hybrid Simulation Model for Failure Prediction in Crowdsourced Software Development

A typical crowdsourcing software development(CSD) marketplace consists of a list of software tasks as service demands and a pool of freelancer developers as service suppliers. Highly dynamic and competitive CSD market places may result in task failure due to unforeseen risks, such as increased competition over shared worker supply, or uncertainty associated with workers' experience and skills, and so on. To improve CSD effectiveness, it is essential to better understand and plan with respect to dynamic worker characteristics and risks associated with CSD processes. In this paper, we present a hybrid simulation model, CrowdSim, to forecast crowdsourcing task failure risk in competitive CSD platforms. CrowdSim is composed of three layered components: the macro-level reflects the overall crowdsourcing platform based on system dynamics,the meso-level represents the task life cycle based on discrete event simulation, and the micro-level models the crowd workers' decision-making processes based on agent-based simulation. CrowdSim is evaluated through three CSD decision scenarios to demonstrate its effectiveness, using a real-world historical dataset and the results demonstrate CrowdSim's potential in empowering crowdsourcing managers to explore crowdsourcing outcomes with respect to different task scheduling options.

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arXiv2020

Stakeholder identification for a structured release planning approach in the automotive domain

Context: In regulated domains like automotive, release planning is a complex process. The agreement between traditional product development processes for hardware as well as mechanic systems and agile development approaches for software development is a major challenge. Especially the creation and synchronization of a release plan is challenging. Objective: The aim of this work is to present identified stakeholders of a release plan as an appropriate approach to create transparency in release planning in the automotive domain. Method: Action research to elaborate relevant stakeholders for release planning was conducted at Dr. Ing. h. c. F. Porsche AG. Results: We present a detailed overview of identified stakeholders due to release planning as well as their required content and added value regarding to two pilot projects. The results confirm the fact that almost every stakeholder is involved in a release plan in a certain way. Conclusions: Release planning within a complex project environment and complicated customer constellations is difficult to manage. We discuss how the presented stakeholders could meet with the given conditions in the automotive domain. With this contribution, identified stakeholders of release planning from hardware and software point of view is introduced. It helps to reach transparency and to handle the given complexity.

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arXiv2020

Beyond Accuracy: ROI-driven Data Analytics of Empirical Data

This vision paper demonstrates that it is crucial to consider Return-on-Investment (ROI) when performing Data Analytics. Decisions on "How much analytics is needed"? are hard to answer. ROI could guide for decision support on the What?, How?, and How Much? analytics for a given problem. Method: The proposed conceptual framework is validated through two empirical studies that focus on requirements dependencies extraction in the Mozilla Firefox project. The two case studies are (i) Evaluation of fine-tuned BERT against Naive Bayes and Random Forest machine learners for binary dependency classification and (ii) Active Learning against passive Learning (random sampling) for REQUIRES dependency extraction. For both the cases, their analysis investment (cost) is estimated, and the achievable benefit from DA is predicted, to determine a break-even point of the investigation. Results: For the first study, fine-tuned BERT performed superior to the Random Forest, provided that more than 40% of training data is available. For the second, Active Learning achieved higher F1 accuracy within fewer iterations and higher ROI compared to Baseline (Random sampling based RF classifier). In both the studies, estimate on, How much analysis likely would pay off for the invested efforts?, was indicated by the break-even point. Conclusions: Decisions for the depth and breadth of DA of empirical data should not be made solely based on the accuracy measures. Since ROI-driven Data Analytics provides a simple yet effective direction to discover when to stop further investigation while considering the cost and value of the various types of analysis, it helps to avoid over-analyzing empirical data.

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arXiv2019

Optimization in Software Engineering -- A Pragmatic Approach

Empirical software engineering is concerned with the design and analysis of empirical studies that include software products, processes, and resources. Optimization is a form of data analytics in support of human decision-making. Optimization methods are aimed to find the best decision alternatives. Empirical studies serve both as a model and as data input for optimization. In addition, the complexity of the models used for optimization trigger further studies on explaining and validating the results in real-world scenarios. The goal of this chapter is to give an overview of the as-is and of the to-be usage of optimization in software engineering. The emphasis is on pragmatic use of optimization, and not so much on describing the most recent algorithmic innovations and tool developments. The usage of optimization covers a wide range of questions from different types of software engineering problems along the whole life-cycle. To facilitate its more comprehensive and more effective usage, a checklist for a guided process is described. The chapter uses a running example Asymmetric Release Planning to illustrate the whole process. A Return-on-Investment analysis is proposed as part of the problem scoping. This helps to decide on the depth and breadth of analysis in relation to the effort needed to run the analysis and the projected value of the solution.

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arXiv2019

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 app's 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 app's release strategy is a factor that affects the ongoing success of mobile apps.

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arXiv2019

Mining Treatment-Outcome Constructs from Sequential Software Engineering Data

Many investigations in empirical software engineering look at sequences of data resulting from development or management processes. In this paper, we propose an analytical approach called the Gandhi-Washington Method (GWM) to investigate the impact of recurring events in software projects. GWM takes an encoding of events and activities provided by a software analyst as input. It uses regular expressions to automatically condense and summarize information and infer treatments. Relating the treatments to the outcome through statistical tests, treatment-outcome constructs are automatically mined from the data. The output of GWM is a set of treatment-outcome constructs. Each treatment in the set of mined constructs is significantly different from the other treatments considering the impact on the outcome and/or is structurally different from other treatments considering the sequence of events. We describe GWM and classes of problems to which GWM can be applied. We demonstrate the applicability of this method for empirical studies on sequences of file editing, code ownership, and release cycle time.

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arXiv2019

Asymmetric Release Planning-Compromising Satisfaction against Dissatisfaction

Maximizing satisfaction from offering features as part of the upcoming release(s) is different from minimizing dissatisfaction gained from not offering features. This asymmetric behavior has never been utilized for product release planning. We study Asymmetric Release Planning (ARP) by accommodating asymmetric feature evaluation. We formulated and solved ARP as a bi-criteria optimization problem. In its essence, it is the search for optimized trade-offs between maximum stakeholder satisfaction and minimum dissatisfaction. Different techniques including a continuous variant of Kano analysis are available to predict the impact on satisfaction and dissatisfaction with a product release from offering or not offering a feature. As a proof of concept, we validated the proposed solution approach called Satisfaction-Dissatisfaction Optimizer (SDO) via a real-world case study project. From running three replications with varying effort capacities, we demonstrate that SDO generates optimized trade-off solutions being (i) of a different value profile and different structure, (ii) superior to the application of random search and heuristics in terms of quality and completeness, and (iii) superior to the usage of manually generated solutions generated from managers of the case study company. A survey with 20 stakeholders evaluated the applicability and usefulness of the generated results.

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arXiv2018

Status Quo in Requirements Engineering: A Theory and a Global Family of Surveys

Requirements Engineering (RE) has established itself as a software engineering discipline during the past decades. While researchers have been investigating the RE discipline with a plethora of empirical studies, attempts to systematically derive an empirically-based theory in context of the RE discipline have just recently been started. However, such a theory is needed if we are to define and motivate guidance in performing high quality RE research and practice. We aim at providing an empirical and valid foundation for a theory of RE, which helps software engineers establish effective and efficient RE processes. We designed a survey instrument and theory that has now been replicated in 10 countries world-wide. We evaluate the propositions of the theory with bootstrapped confidence intervals and derive potential explanations for the propositions. We report on the underlying theory and the full results obtained from the replication studies with participants from 228 organisations. Our results represent a substantial step forward towards developing an empirically-based theory of RE giving insights into current practices with RE processes. The results reveal, for example, that there are no strong differences between organisations in different countries and regions, that interviews, facilitated meetings and prototyping are the most used elicitation techniques, that requirements are often documented textually, that traces between requirements and code or design documents is common, requirements specifications themselves are rarely changed and that requirements engineering (process) improvement endeavours are mostly intrinsically motivated. Our study establishes a theory that can be used as starting point for many further studies for more detailed investigations. Practitioners can use the results as theory-supported guidance on selecting suitable RE methods and techniques.

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arXiv2018

A Longitudinal Study of Identifying and Paying Down Architectural Debt

Architectural debt is a form of technical debt that derives from the gap between the architectural design of the system as it "should be" compared to "as it is". We measured architecture debt in two ways: 1) in terms of system-wide coupling measures, and 2) in terms of the number and severity of architectural flaws. In recent work it was shown that the amount of architectural debt has a huge impact on software maintainability and evolution. Consequently, detecting and reducing the debt is expected to make software more amenable to change. This paper reports on a longitudinal study of a healthcare communications product created by Brightsquid Secure Communications Corp. This start-up company is facing the typical trade-off problem of desiring responsiveness to change requests, but wanting to avoid the ever-increasing effort that the accumulation of quick-and-dirty changes eventually incurs. In the first stage of the study, we analyzed the status of the "before" system, which indicated the impacts of change requests. This initial study motivated a more in-depth analysis of architectural debt. The results of this analysis were used to motivate a comprehensive refactoring of the software system. The third phase of the study was a follow-on architectural debt analysis which quantified the improvements made. Using this quantitative evidence, augmented by qualitative evidence gathered from in-depth interviews with Brightsquid's architects, we present lessons learned about the costs and benefits of paying down architecture debt in practice.

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arXiv2018

ESSMArT Way to Manage User Requests

Quality and market acceptance of software products is strongly influenced by responsiveness to user requests. Once a request is received from a customer, decisions need to be made if the request should be escalated to the development team. Once escalated, the ticket must be formulated as a development task and be assigned to a developer. To make the process more efficient and reduce the time between receiving and escalating the user request, we aim to automate of the complete user request management process. We propose a holistic method called ESSMArT. The methods performs text summarization, predicts ticket escalation, creates the title and content of the ticket used by developers, and assigns the ticket to an available developer. We internally evaluated the method by 4,114 user tickets from Brightsquid and their secure health care communication plat- form Secure-Mail. We also perform an external evaluation on the usefulness of the approach. We found that supervised learning based on context specific data performs best for extractive summarization. For predicting escalation of tickets, Random Forest trained on a combination of conversation and extractive summarization is best with highest precision (of 0.9) and recall (of 0.55). From external evaluation we found that ESSMArT provides suggestions that are 71% aligned with human ones. Applying the prototype implementation to 315 user requests resulted in an average time reduction of 9.2 minutes per request. ESSMArT helps to make ticket management faster and with reduced effort for human experts. ESSMArT can help Brightsquid to (i) minimize the impact of staff turnover and (ii) shorten the cycle from an issue being reported to an assignment to a developer to fix it.

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arXiv2017

What Works Better? A Study of Classifying Requirements

Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Naive Bayes for the sub-classification of NFRs.

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