Software Engineering (cs.SE)

Abstract: Prescriptive business process monitoring provides decision support to process managers on when and how to adapt an ongoing business process to prevent or mitigate an undesired process outcome. We focus on the problem of automatically reconciling the trade-off between prediction accuracy and prediction earliness in determining when to adapt. Adaptations should happen sufficiently early to provide enough lead time for the adaptation to become effective. However, earlier predictions are typically less accurate than later predictions. This means that acting on less accurate predictions may lead to unnecessary adaptations or missed adaptations. Different approaches were presented in the literature to reconcile the trade-off between prediction accuracy and earliness. So far, these approaches were compared with different baselines, and evaluated using different data sets or even confidential data sets. This limits the comparability and replicability of the approaches and makes it difficult to choose a concrete approach in practice. We perform a comparative evaluation of the main alternative approaches for reconciling the trade-off between prediction accuracy and earliness. Using four public real-world event log data sets and two types of prediction models, we assess and compare the cost savings of these approaches. The experimental results indicate which criteria affect the effectiveness of an approach and help us state initial recommendations for the selection of a concrete approach in practice.

Abstract: Automated logging statement generation techniques facilitate developers in writing appropriate logging statements that document software behaviors. Current retrieval-based and learning-based logging methods fail to provide accurate logging statements in complex software. Although existing large language models (LLMs) might be a good fit for the task due to their great success in natural language generation and programming language comprehension, their effectiveness and generalization capabilities have not been explored. To this end, this paper performs the first extensive study on applying LLMs for logging statement generation. We build LogBench, the first logging statement generation dataset. On LogBench, we evaluate the effectiveness and generalization capabilities of eight state-of-the-art LLMs, which include general-purpose and code-specific models ranging from 60M to 175B in size. Specifically, we evaluate LLM's logging effectiveness by studying 1) their ability to decide logging ingredients, 2) the impact of the internal characteristics of LLMs, and 3) the influence of external factors. We further evaluate LLM's logging generalization capabilities using unseen data derived from code transformation techniques. Our study demonstrates that existing LLMs fall short of practical requirements for generating proper logging statement texts. We also disclose the impact of internal characteristics and external factors for LLMs in automated logging. In addition, we observe that existing LLMs cannot generalize to logging unseen code, revealing their unsatisfactory generalization capabilities. Based on our findings, we further discuss three implications that can enhance logging statement generation in the future, such as developing a unified metric for logging quality, incorporating shareable code knowledge into LLMs, and devising suitable prompts.

Abstract: Traditional coverage grey-box fuzzers perform a breadth-first search of the state space of Program Under Test (PUT). This aimlessness wastes a lot of computing resources. Directed grey-box fuzzing focuses on the target of PUT and becomes one of the most popular topics of software testing. The early termination of unreachable test cases is a method to improve directed grey-box fuzzing. However, existing solutions have two problems: firstly, reachability analysis needs to introduce extra technologies (e.g., static analysis); secondly, the performance of reachability analysis and auxiliary technologies lack versatility. We propose FGo, a probabilistic exponential cut-the-loss directed grey-box fuzzer. FGo terminates unreachable test cases early with exponentially increasing probability. Compared to other technologies, FGo makes full use of the unreachable information contained in iCFG and doesn't generate any additional overhead caused by reachability analysis. Moreover, it is easy to generalize to all PUT. This strategy based on probability is perfectly adapted to the randomness of fuzzing. The experiment results show that FGo is 106% faster than AFLGo in reproducing crashes. We compare multiple parameters of probabilistic exponential cut-the-loss algorithm and analyze them in detail. In addition, for enhancing the inerpretability of FGo, this paper discusses the difference between the theoretical performance and the practical performance of probabilistic exponential cut-the-loss algorithm.

Abstract: [Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time.

Abstract: In this paper, the adoption patterns of Generative Artificial Intelligence (AI) tools within software engineering are investigated. Influencing factors at the individual, technological, and societal levels are analyzed using a mixed-methods approach for an extensive comprehension of AI adoption. An initial structured interview was conducted with 100 software engineers, employing the Technology Acceptance Model (TAM), the Diffusion of Innovations theory (DOI), and the Social Cognitive Theory (SCT) as guiding theories. A theoretical model named the Human-AI Collaboration and Adaptation Framework (HACAF) was deduced using the Gioia Methodology, characterizing AI adoption in software engineering. This model's validity was subsequently tested through Partial Least Squares - Structural Equation Modeling (PLS-SEM), using data collected from 183 software professionals. The results indicate that the adoption of AI tools in these early integration stages is primarily driven by their compatibility with existing development workflows. This finding counters the traditional theories of technology acceptance. Contrary to expectations, the influence of perceived usefulness, social aspects, and personal innovativeness on adoption appeared to be less significant. This paper yields significant insights for the design of future AI tools and supplies a structure for devising effective strategies for organizational implementation.