Jacob Krüger

Dmitri Bershadskyy, Jacob Krüger, Gül Çalıklı et al.

In software-engineering research, many empirical studies are conducted with open-source or industry developers. However, in contrast to other research communities like economics or psychology, only few experiments use financial incentives (i.e., paying money) as a strategy to motivate participants' behavior and reward their performance. The most recent version of the SIGSOFT Empirical Standards mentions payouts only for increasing participation in surveys, but not for mimicking real-world motivations and behavior in experiments. Within this article, we report a controlled experiment in which we tackled this gap by studying how different financial incentivization schemes impact developers. For this purpose, we first conducted a survey on financial incentives used in the real-world, based on which we designed three incentivization schemes: (1) a performance-dependent scheme that employees prefer, (2) a scheme that is performance-independent, and (3) a scheme that mimics open-source development. Then, using a between-subject experimental design, we explored how these three schemes impact participants' performance. Our findings indicate that the different schemes can impact participants' performance in software-engineering experiments. Due to the small sample sizes, our results are not statistically significant, but we can still observe clear tendencies. Our contributions help understand the impact of financial incentives on participants in experiments as well as real-world scenarios, guiding researchers in designing experiments and organizations in compensating developers.

Sebastian Nielebock, Paul Blockhaus, Jacob Krüger et al.

Modern software development heavily relies on the reuse of functionalities through Application Programming Interfaces (APIs). However, client developers can have issues identifying the correct usage of a certain API, causing misuses accompanied by software crashes or usability bugs. Therefore, researchers have aimed at identifying API misuses automatically by comparing client code usages to correct API usages. Some techniques rely on certain API-specific graph-based data structures to improve the abstract representation of API usages. Such techniques need to compare graphs, for instance, by computing distance metrics based on the minimal graph edit distance or the largest common subgraphs, whose computations are known to be NP-hard problems. Fortunately, there exist many abstractions for simplifying graph distance computation. However, their applicability for comparing graph representations of API usages has not been analyzed. In this paper, we provide a comparison of different distance algorithms of API-usage graphs regarding correctness and runtime. Particularly, correctness relates to the algorithms' ability to identify similar correct API usages, but also to discriminate similar correct and false usages as well as non-similar usages. For this purpose, we systematically identified a set of eight graph-based distance algorithms and applied them on two datasets of real-world API usages and misuses. Interestingly, our results suggest that existing distance algorithms are not reliable for comparing API usage graphs. To improve on this situation, we identified and discuss the algorithms' issues, based on which we formulate hypotheses to initiate research on overcoming them.

Sebastian Nielebock, Paul Blockhaus, Jacob Krüger et al.

Many developers and organizations implement apps for Android, the most widely used operating system for mobile devices. Common problems developers face are the various hardware devices, customized Android variants, and frequent updates, forcing them to implement workarounds for the different versions and variants of Android APIs used in practice. In this paper, we contribute the Android Compatibility checkS dataSet (AndroidCompass) that comprises changes to compatibility checks developers use to enforce workarounds for specific Android versions in their apps. We extracted 80,324 changes to compatibility checks from 1,394 apps by analyzing the version histories of 2,399 projects from the F-Droid catalog. With AndroidCompass, we aim to provide data on when and how developers introduced or evolved workarounds to handle Android incompatibilities. We hope that AndroidCompass fosters research to deal with version incompatibilities, address potential design flaws, identify security concerns, and help derive solutions for other developers, among others-helping researchers to develop and evaluate novel techniques, and Android app as well as operating-system developers in engineering their software.