Mohamed Soliman

Yikun Li, Mohamed Soliman, Paris Avgeriou

Technical debt refers to the consequences of sub-optimal decisions made during software development that prioritize short-term benefits over long-term maintainability. Self-Admitted Technical Debt (SATD) is a specific form of technical debt, explicitly documented by developers within software artifacts such as source code comments and commit messages. As SATD can hinder software development and maintenance, it is crucial to estimate the effort required to repay it so that we can effectively prioritize it. However, we currently lack an understanding of SATD repayment, and more importantly, we lack approaches that can automatically estimate the repayment effort of SATD based on its textual description. To bridge this gap, we have curated a comprehensive dataset of 341,740 SATD items from 2,568,728 commits across 1,060 Apache repositories and analyzed the repayment effort comparing SATD vs. non-SATD items, as well as different types of SATD items. Furthermore, we proposed an innovative approach for Predicting Repayment Effort of SATD using Textual Information, named PRESTI. Our findings show that different types of SATD require varying levels of repayment effort, with code/design, requirement, and test debt demanding greater effort compared to non-SATD items, while documentation debt requires less. We have evaluated our approaches, particularly BERT- and TextCNN-based models, which outperform traditional machine learning methods and the baseline in estimating repayment effort. Additionally, we summarize keywords associated with varying levels of repayment effort that occur during SATD repayment. Our work aims to enhance SATD repayment prioritization and resource allocation, thereby improving software development and maintainability.

Yikun Li, Mohamed Soliman, Paris Avgeriou

Technical debt refers to taking shortcuts to achieve short-term goals, which might negatively influence software maintenance in the long-term. There is increasing attention on technical debt that is admitted by developers in source code comments (termed as self-admitted technical debt or SATD). But SATD in issue trackers is relatively unexplored. We performed a case study, where we manually examined 500 issues from two open source projects (i.e. Hadoop and Camel), which contained 152 SATD items. We found that: 1) eight types of technical debt are identified in issues, namely architecture, build, code, defect, design, documentation, requirement, and test debt; 2) developers identify technical debt in issues in three different points in time, and a small part is identified by its creators; 3) the majority of technical debt is paid off, 4) mostly by those who identified it or created it; 5) the median time and average time to repay technical debt are 872.3 and 25.0 hours respectively.

Samira Khorshidi, Azadeh Nikfarjam, Suprita Shankar et al.

Knowledge graphs (KGs) are foundational to many AI applications, but maintaining their freshness and completeness remains costly. We present ODKE+, a production-grade system that automatically extracts and ingests millions of open-domain facts from web sources with high precision. ODKE+ combines modular components into a scalable pipeline: (1) the Extraction Initiator detects missing or stale facts, (2) the Evidence Retriever collects supporting documents, (3) hybrid Knowledge Extractors apply both pattern-based rules and ontology-guided prompting for large language models (LLMs), (4) a lightweight Grounder validates extracted facts using a second LLM, and (5) the Corroborator ranks and normalizes candidate facts for ingestion. ODKE+ dynamically generates ontology snippets tailored to each entity type to align extractions with schema constraints, enabling scalable, type-consistent fact extraction across 195 predicates. The system supports batch and streaming modes, processing over 9 million Wikipedia pages and ingesting 19 million high-confidence facts with 98.8% precision. ODKE+ significantly improves coverage over traditional methods, achieving up to 48% overlap with third-party KGs and reducing update lag by 50 days on average. Our deployment demonstrates that LLM-based extraction, grounded in ontological structure and verification workflows, can deliver trustworthiness, production-scale knowledge ingestion with broad real-world applicability. A recording of the system demonstration is included with the submission and is also available at https://youtu.be/UcnE3_GsTWs.

Ruiyin Li, Peng Liang, Mohamed Soliman et al.

Architecture erosion (AEr) can adversely affect software development and has received significant attention in the last decade. However, there is an absence of a comprehensive understanding of the state of research about the reasons and consequences of AEr, and the countermeasures to address AEr. This work aims at systematically investigating, identifying, and analyzing the reasons, consequences, and ways of detecting and handling AEr. With 73 studies included, the main results are as follows: (1) AEr manifests not only through architectural violations and structural issues but also causing problems in software quality and during software evolution; (2) non-technical reasons that cause AEr should receive the same attention as technical reasons, and practitioners should raise awareness of the grave consequences of AEr, thereby taking actions to tackle AEr-related issues; (3) a spectrum of approaches, tools, and measures has been proposed and employed to detect and tackle AEr; and (4) three categories of difficulties and five categories of lessons learned on tackling AEr were identified. The results can provide researchers a comprehensive understanding of AEr and help practitioners handle AEr and improve the sustainability of their architecture. More empirical studies are required to investigate the practices of detecting and addressing AEr in industrial settings.

Mohamed Soliman, Marion Wiese, Yikun Li et al.

Software engineers need relevant and up-to-date architectural knowledge (AK), in order to make well-founded design decisions. However, finding such AK is quite challenging. One pragmatic approach is to search for AK on the web using traditional search engines (e.g. Google); this is common practice among software engineers. Still, we know very little about what AK is retrieved, from where, and how useful it is. In this paper, we conduct an empirical study with 53 software engineers, who used Google to make design decisions using the Attribute-Driven-Design method. Based on how the subjects assessed the nature and relevance of the retrieved results, we determined how effective web search engines are to find relevant architectural information. Moreover, we identified the different sources of AK on the web and their associated AK concepts.

Ruiyin Li, Peng Liang, Mohamed Soliman et al.

As software systems evolve, their architecture is meant to adapt accordingly by following the changes in requirements, the environment, and the implementation. However, in practice, the evolving system often deviates from the architecture, causing severe consequences to system maintenance and evolution. This phenomenon of architecture erosion has been studied extensively in research, but not yet been examined from the point of view of developers. In this exploratory study, we look into how developers perceive the notion of architecture erosion, its causes and consequences, as well as tools and practices to identify and control architecture erosion. To this end, we searched through several popular online developer communities for collecting data of discussions related to architecture erosion. Besides, we identified developers involved in these discussions and conducted a survey with 10 participants and held interviews with 4 participants. Our findings show that: (1) developers either focus on the structural manifestation of architecture erosion or on its effect on run-time qualities, maintenance and evolution; (2) alongside technical factors, architecture erosion is caused to a large extent by non-technical factors; (3) despite the lack of dedicated tools for detecting architecture erosion, developers usually identify erosion through a number of symptoms; and (4) there are effective measures that can help to alleviate the impact of architecture erosion.