@@ -30,7 +30,7 @@ function HeroContent() {
3030 return (
3131 < div className = "text-center" >
3232 < h1 className = "text-balance text-5xl font-semibold tracking-tight text-gray-900 sm:text-7xl" >
33- ArDoCo Trace View
33+ ARDoCo Trace View
3434 </ h1 >
3535 < div className = "mt-10 flex items-center justify-center gap-x-6" >
3636 < Link
@@ -40,7 +40,7 @@ function HeroContent() {
4040 Start
4141 </ Link >
4242 < Link href = "https://ardoco.de/" className = " font-semibold text-black-600 hover:text-black" >
43- About ArDoCo < span > →</ span >
43+ About < span > →</ span >
4444 </ Link >
4545 </ div >
4646 </ div >
@@ -50,33 +50,37 @@ function HeroContent() {
5050// About Section Component
5151function AboutSection ( ) {
5252 return (
53- < div className = "flex items-center justify-center gap-x-6 dark:text-white px-12 pb-12 lg:px-16 flex-col" >
54- < h2 className = "text-left py-4 text-xl self-start" > ArDoCo - Architecture Documentation Consistency</ h2 >
55- < p className = "py-2" >
56- In this research project, we aim to provide consistency analyses between different kinds of
57- documentation, namely formal models and informal (textual) documentation.
58- </ p >
59- < p className = "py-2" >
60- Documenting the architecture of a software system is important, especially to capture reasoning
61- and design decisions. A lot of tacit knowledge is easily lost when the documentation is
62- incomplete, resulting in threats for the software system’s success and increased costs. However,
63- software architecture documentation is often missing or outdated. One explanation for this
64- phenomenon is the tedious and costly process of creating documentation in comparison to
65- (perceived) low benefits. With our project, we want to step forward in our long-term vision,
66- where we plan to persist information from any sources, e.g., from whiteboard discussions, to
67- avoid losing crucial information about a system. A core problem in this vision is the possible
68- inconsistency of information from different sources. A major challenge of ensuring consistency
69- is the consistency between formal artefacts, i.e., models, and informal documentation. We plan to
70- address consistency analyses between models and textual natural language artefacts using natural
71- language understanding and plan to include knowledge bases to improve these analyses. After
72- extracting information out of the natural language documents, we plan to create traceability
73- links and check whether statements within the textual documentation are consistent with the
74- software architecture models.
75- </ p >
76- < p className = "py-2" >
77- ArDoCo is actively developed by researchers of the Modelling for Continuous Software Engineering
78- (MCSE) group of KASTEL - Institute of Information Security and Dependability at the KIT.
79- </ p >
53+ < div className = "flex justify-center dark:text-white px-6 sm:px-12 pb-12 lg:px-16" >
54+ < div className = "w-full max-w-5xl text-left bg-gray-100/70 dark:bg-gray-800/60 rounded-lg p-6 sm:p-8 shadow-sm" >
55+ < h2 className = "py-4 text-xl" > ARDoCo - Automating Requirements and Documentation Comprehension</ h2 >
56+ < p className = "py-2" >
57+ In this research project, we aim to provide consistency analyses between different kinds of
58+ documentation, namely formal models and informal (textual) documentation.
59+ </ p >
60+ < p className = "py-2" >
61+ Documenting the architecture of a software system is important, especially to capture reasoning
62+ and design decisions. A lot of tacit knowledge is easily lost when the documentation is
63+ incomplete, resulting in threats for the software system’s success and increased costs. However,
64+ software architecture documentation is often missing or outdated. One explanation for this
65+ phenomenon is the tedious and costly process of creating documentation in comparison to
66+ (perceived) low benefits. With our project, we want to step forward in our long-term vision,
67+ where we plan to persist information from any sources, e.g., from whiteboard discussions, to
68+ avoid losing crucial information about a system. A core problem in this vision is the possible
69+ inconsistency of information from different sources. A major challenge of ensuring consistency
70+ is the consistency between formal artefacts, i.e., models, and informal documentation. We plan to
71+ address consistency analyses between models and textual natural language artefacts using natural
72+ language understanding and plan to include knowledge bases to improve these analyses. After
73+ extracting information out of the natural language documents, we plan to create traceability
74+ links and check whether statements within the textual documentation are consistent with the
75+ software architecture models.
76+ </ p >
77+ < p className = "py-2" >
78+ ARDoCo is actively developed by researchers of the{ ' ' }
79+ < Link href = "https://mcse.kastel.kit.edu/" className = "underline underline-offset-2 hover:opacity-80" > Modelling for Continuous Software Engineering</ Link >
80+ { ' ' } group of KASTEL - Institute of Information Security and Dependability at the{ ' ' }
81+ < Link href = "https://www.kit.edu/" className = "underline underline-offset-2 hover:opacity-80" > KIT</ Link > .
82+ </ p >
83+ </ div >
8084 </ div >
8185 ) ;
8286}
0 commit comments